M ultiple O rg anisations of E ven ts in M em ory

Transcription

1 Ó M E M O R Y, , 5 (5 ), 5 69 ±59 9 M ultiple O rg anisations of E ven ts in M em ory Juliana S. L a nc aste r M orris Brow n C ollege, Atlanta, U SA L a w rence W. B arsa lou E m ory U niversity, A tlanta, U SA T heories of m em ory organisation propose that activity know ledge organises autob iog raphical m em ory g lobally. A ccord ing to these view s, m em o ries that sh are a participant, location, or tim e are only organised together if they also share an activity. If they do not, they are nested w ithin their respective activity organisations locally rather than being organised together globally. T w o experim ents that asse ssed people s clustering o f labo rato ry events con sistently o b tained find in gs that con tradict th is v iew. B o th experim ents fo u nd th at p eo p le organise ev ent m em o ries globally in non -activity clusters, cross-classify events into m ultiple organisations, and pivot betw een activity and non-activity clusters. C onsistent w ith studies of naturalistic events, these stud ies o f labo ratory even ts indicate that p eop le cro ssclassify event m em ories sim ultaneously into m ultiple global organisations. INTRODUCTION M em ory for events has received increasing attention from m em ory researchers, as m uch recent w ork illustrates (e.g. for review s, see C onw ay, 1990b; N eisser, 1982; N eisser & W inograd, 1988; N elson, 1986; R ubin, 1986). A lthough this w ork has addressed num erous im portant aspects of autobiographical m em ory, it has provided little inform ation about its organisation. T o the extent that organisation has received attention, one account has dom inated. A ccording to this view, know ledge of activity types organises event m em ories as a by-product of the com prehension process (K olodner, 1978, 1980, 1983a,b, 1984; R eiser, 1983, 1986; R eiser, B lack, & A belson, 1985; R eiser, B lack, & K alam arides, 1987; Schank, 1982; Schank & K olodner, 1979; also see B arsalou, 1995). R e q u es ts fo r re prints sh ou ld be se n t to L a w re nc e W. B a rsa lou, D e pa rtm e n t o f P sy ch o lo gy, E m o ry U nive rs ity, A tla nta, G A , U S A. E m a il: ba rs a lo em o ry.e du T his article rep orts a subset of the experim ents fro m the first author s doctoral dissertation perform ed at Em ory U niversity (L an caster, 1985). G rants IST , IR I , an d SB R fro m the N ational Scien ce Foundation to the second author supported this w ork. W e are grateful to D aniel S ew ell for his contributions to this project, an d to M artin C onw ay and tw o anonym ous review ers fo r their helpfu l com m ents on an earlier draft of this paper Psychology Press L td

2 570 LANCASTER AND BARSALOU FIG 1. A n exam ple of even t org anisation accord ing to the strong activity view. O nly activities org an ise events globally, w ith other org anisations, such as participants and locations, em bedded locally. L is location, an d O is object. B ecause events involving the sam e activity are com prehended w ith the sam e abstract know ledge, m em ories of them becom e integrated together w ith it. For exam ple, all events that involve eating at a restaurant are stored together w ith abstract know ledge about eating at restaurants; all events that involve going to m ovies are stored together w ith abstract know ledge about going to m ovies; and so forth. T his abstract know ledge could take the form of scripts (Schank & A belson, 1977), M O Ps (Schank, 1982), E -M O Ps (K olodner, 1978, 1980, 1983a,b, 1984), or contexts (R eiser et al., 1985; R eiser et al., 1987). W ithin one of these abstract form s of know ledge, event m em ories that share the sam e activity are organised by differences am ong them (e.g. participants, locations). For exam ple, different m em ories about eating at restaurants m ight be further organised into participant subclusters, such as eating at restaurants w ith fam ily, w ith friends, w ith co-w orkers, and so forth. In turn, these clusters could contain still m ore specific subclusters, such as clusters of events for eating C hinese food w ith friends, eating Indian food w ith friends, and so forth.

3 EVENT ORGANISATION 571 Figure 1 show s a set of events organised in this m anner. A s can be seen, activities provide the highest level of event organisation, w ith events being grouped m ost globally into those that involve sw im m ing, those that involve eating, and so forth. O ther global organisations of events do not occur. For exam ple, events sharing the sam e participant (e.g. fam ily) are not organised together but instead are distributed in tw o different activity organisations, w ith separate subclusters of events developing around participants in each activity organisation. For exam ple, one subcluster of events that involves friends has form ed under sw im m ing, and another has form ed under eating. W ith further events, still m ore specific subclusters could occur, such as eating w ith friends at restaurants. Im portantly, these subclusters are defined locally w ith respect to particular activities. For exam ple, the tw o clusters of events that involve friends are local w ith respect to sw im m ing and eating. T here is no single cluster of events that involves friends independent of the activity. C onsequently, this view predicts that a cluster of events sharing a participant should alw ays share an activity as w ell. In retrieving events about friends, for exam ple, people m ight only retrieve events that involve eating and fail to retrieve events that involve any other activity, such as sw im m ing. R etrieving an event m em ory from such an organisation first requires identifying the activity. If the retrieval cue specifies an activity (e.g. ``eating in the cue ``eating C hinese food w ith friends ), search becom es constrained to the cluster of events that all involve eating. If a retrieval cue fails to specify an activity (e.g. recall an event at the beach w ith fam ily), then inference strategies identify candidate activities to search (Kolodner, 1984; R eiser et al., 1985). For exam ple, sw im m ing w ould be a reasonable activity to infer for an event at the beach. O n inferring an activity, know n aspects of the event (e.g. participants, location, objects) guide search through the m em ories of the activity, specifying the path to take through the activity organisation. O n accessing the cluster of events that share sw im m ing, for exam ple, search m ight seek a subcluster for beach or fam ily. W hen such details are m issing and thereby prevent further search tow ards an event m em ory, various cue elaboration strategies provide plausible inferences about w hat the details m ight be (K olodner, 1983b; R eiser et al., 1987). O nce an event m em ory is found, search ends. If the initially selected activity does not produce a successful retrieval, these search and inference strategies proceed iteratively through other activity organisations. For exam ple, if the sought-after event at the beach is not found under sw im m ing, it m ight be searched for under fishing. Im portantly, these accounts of activity organisation predict that people cannot begin to search for an event m em ory w ithout first having an activity cue that specifies the activity organisation to search. A particular location or participant cannot provide direct access to a sought-after event. Instead, such cues provide a m eans of inferring a probable activity organisation to search, and search can only begin once an activity has been selected.

4 572 LANCASTER AND BARSALOU In contrast, other organisational fram ew orks m ake different predictions. O ne alternative schem e assum es that event m em ories are cross-classified sim ultaneously in a variety of global organisations, including organisations for location, participant, tim e, and objectsð as w ell as for activity (e.g. B arsalou, 1988). Figure 2 show s the sam e set of events as in Fig. 1 organised in this alternative m anner. A s can be seen, any event can be accessed directly by a num ber of retrieval cues. For exam ple, E vent 1 can be accessed directly from fam ily, beach, or snorkelð it is not necessary to know or infer an activity initially. Furtherm ore, global clusters of events develop for other attributes besides activity. For exam ple, all m em ories that involve friends in Fig. 2 are organised into a single global cluster. (R ecall that these m em ories w ere distributed in tw o unrelated subclusters in Fig. 1.) Sim ilarly, global clusters exist in Fig. 2 for fam ily, beach, and Indian food. In general, this cross-classification approach to event organisation assum es that m ultiple organisations provide direct access to events and establish a w ide variety of global event clusters. 1 R ecent findings suggest that people use other conceptual structures besides activities to organise events globally (for review s see C onw ay, 1990b; C onw ay & R ubin, 1993). For exam ple, people som etim es organise events globally w ith goal-derived categories (C onw ay, 1990a) or em otion concepts (C onw ay, 1990). O n other occasions, people organise events w ith various tem poral structures and schem ata (A nderson & C onw ay, 1993; E ldridge, B arnard, & B ekerian, 1994). Several investigators have found that extended tem poral events are particularly im portant in global event organisation (B arsalou, 1988; B row n, Shevell, & R ips, 1986; C onw ay & B ekerian, 1987). For exam ple, people organise events according to extended stays in locations (e.g. w hen I lived in C alifornia), extended periods of schooling (e.g. w hen I w as in college), and extended personal relationships (e.g. m y second m arriage). E xtended events differ from activity types in several w ays (B arsalou, 1988). For exam ple, event m em ories are organised chronologically as parts of a single extended event rather than being organised conceptually as m ultiple, tem porally unrelated instances of an activity type. T ogether, these findings suggest that non-activity organisations can organise event m em ories globally. Finally, an interm ediate view is that people can cross-classify events in m ultiple organisations globally but that activity organisations provide the dom inant form of organisation. Follow ing the intuitions behind the activity v iew of K olo dner, R eiser, an d S chan k, activitie s p rov ide a pro m inen t 1 F igu re 2 o m its m u c h im p ortan t stru c ture a b ou t th e org a n isa tio n o f co n c ep tu a l d om a ins. F or exam ple, Fig. 2 gro ups raft together w ith Indian and C hinese food, failing to acknow ledge that the tw o ty p e s o f foo d fo rm a c on c e ptua l su bc lu ste r. A s B a rs a lo u (1 98 8) illustra tes, h o w e v e r, th e pro p er w ay to represent these co ncep tual dom ains is hierarchically. For exam ple, objects m ight be divided into a nim at e a n d in a nim a te o bje ct s, w h ic h m igh t be fu rthe r d iv id ed into still m or e spe c ific c a te g ories, and so fo rth. D epending on how entities are categorised in an even t, an event m em ory m ay point to objects at diffe rent levels in a hierarchy.

5 FIG. 2. A n exam ple of event organisation according to the cross-classification view. The sam e events as in F ig. 1 are organised globally in m ultiple organisations. Events 2 and 5 form a high-sim ilarity cluster sharing an activity, w hereas Events 1 and 4 form a high -sim ilarity cluster sharing no activity. 573

6 574 LANCASTER AND BARSALOU organisation of events. B ecause activities are so central to com prehending ev ents, th ey bec om e high ly inte grated w ith event m em ories. H ow eve r, because events are also processed w ith respect to their locations, participants, tim es, and objects, they also becom e directly integrated w ith these other c on ce ptua l do m ains (B arsa lo u, , ). N e vertheless, if ac tivities typically receive m ore processing than these other dom ains, a greater num ber of links m ay becom e established from them, such that activity organisation dom inates. C onsequently, m ultiple organisations provide direct access to an event, but its activity provides the best access. R esults from E xperim ent 3 in C onw ay and B ekerian (1987) argue against this view, because extended events provided better cues than activities. T he experim ents reported here investigated the extent to w hich non-activity event characteristics organise event m em ories globally and provide direct access to these m em ories unm editated by activity. A ccording to the strong activity position, only activities provide direct access to events. A ccording to the w eak activity position, m ultiple organ isations provide direct access to events, w ith activities providing the best access. A ccording to other cross-classification view s, m ultiple organisations provide direct access, w ith there being no m ajor differences betw een organisations, or w ith som e non-activity organisation providing the dom inant organisation. T he critical m easures in these experim ents concern the clustering of events during recall. W e assum e, like m any previous investigators, that clustering reflects underlying m em ory organisation (e.g. B arsalou & Sew ell, 1985; C hase & E ricsson, 1981; G raesser & M andler, 1975; M andler, 1967; R eitm an, 1976; T ulving, 1962, 1964, 1966; T ulving & Pearlstone, 1966). W hen people attem pt to recall inform ation from long-term m em ory, they access chunks of inform ation sequentially. A s people access each chunk, they report as m any of its m em bers as are accessible. People do not recall one m em ber from one chunk, one m em ber from a second chunk, one m em ber from a third chunk, return to the first chunk and recall a second m em ber, etc. Instead, people retrieve as m any m em bers from the first chunk as possible, proceed to a second chunk, a third chunk, and so forth, rarely returning to a previously accessed chunk. T he logic of our studies rests on these assum ptions about m em ory. If subjects only form global chunks according to activity, then every high-level cluster during recall should share an activity. W ithin an activity cluster, subclusters m ay share a non-activity feature (e.g. E vents 2 and 5 in Fig. 1 share friends); how ever, subjects should not form a cluster that does not share an activity, because this w ould involve retrieving one m em ory at a tim e from different clusters (e.g. to cluster m em ories globally by participant in Fig. 1). In contrast, the organisation in Fig. 2 represents a m uch w ider variety of global chunks, including chunks for participants, locations, and objects. If subjects are organising event m em ories in this m anner, then they could easily produce m any non-activity clusters as w ell as activity clusters.

7 EVENT ORGANISATION 575 T o im plem ent experim ental control over chunking and clustering, w e exam ined people s m em ory for laboratory events. M ost previous studies of event m em ory have observed people s m em ory of natural events. H ow ever, one problem w ith natural events is lack of control over their content, distribution, and processing. L aboratory events have obvious advantages in these regards. O f course the problem w ith laboratory events concerns the generalisability of their results. H ow ever, there are tw o responses to this problem. First, w e have perform ed com parable studies w ith natural events. If w e observe the sam e pattern of results in both lines of w ork, then this pattern is unlikely to reflect a particular m ethod. A s w e shall see in the general discussion, findings from analogous experim ents on the organisation of natural events corroborate our fin dings here for laboratory events (B arsalou, 1988). Second, there are theoretical reasons for believing that the recall organisation of laboratory events should parallel the recall organisation of natural events. W hen presented w ith a set of unfam iliar events to recall, the easiest approach w ould be to use the sam e organisational system for recalling natural events. T he large literature on organisation in the free recall of taxonom ic item s supports this assum ption. A s review ed in C row der (1976, C h.1 0) and Puff (1979), people use w ell-established conceptual taxonom ies in long-term m em ory to encode and retrieve lists of w ords from categories such as birds, furniture, and fruit. W hen people receive random lists of w ords from these catego ries in lab orato ry se ttings, they organ ise them w ith the se w e llestablished taxonom ies. If people sim ilarly have w ell-established organisations for eventsð w hich certainly seem s reasonable given how central events are to everyday activityð then these organisations should m anifest them selves w hen people encounter laboratory events. R obinson s (1986) finding that people use tem p oral organisations of the year to learn laboratory lists incidentally indicates that people use event-related organisations to encode laboratory inform ation. B esides m anipulating type of organisation in the experim ents to follow, w e a lso m a n ip u la te d in c id en tal v ersu s inten tio n al lea rn ing. U nd er na tu ral circum stances, people don t try to m em orise events as they are experiencing them. Instead, people typically acquire inform ation about events incidentally. In each experim ent, therefore, subje cts w ere first exposed to events w hile perform ing an incidental orienting task. Follo w ing this initial presentation, subjects perform ed an unexpected recall of the events, enabling observation of organisational tendencies that m ight occur typically in natural settings w hen people recall events they did not try to learn. A fter this initial recall, subjects w ere told that they w ould be presented w ith the sam e events in a new order and that they w ould be asked subsequently to rem em ber them again. O f interest w as w hether different organisational strategies w ould com e into play w hen subjects tried explicitly to rem em ber the events. This m ulti-trial recall form at enabled us to observe both incidental and intentional m em ory of laboratory events.

8 576 LANCASTER AND BARSALOU EXPERIMENT 1 T he prim ary purpose of this first study w as to explore the extent to w hich four event characteristics dom inate subjects clustering of laboratory events during free recall: activity, participant, location, and tim e. E ach event contained these four types of inform ation and belonged to an event cluster sharing at least one of these characteristics. In the low -sim ilarity list, clusters of events shared one and only one of these characteristics (e.g. several events that w ere instances of presenting an aw ard; several other events that occurred in H aw aii). If activities provide the dom inant organisation of event m em ories, then w e should prim arily observe clustering of those events that share an activityð w e should not see clustering for events that share only a participant, a location, or a tim e. If event m em ories are only stored in organisations for activities, then events that share a participant should be stored in different activity organisations, thereby not being clustered together (e.g. Events 1 and 4 in Fig. 1). Sim ilarly, events sharing only a location or a tim e should be distributed throughout m em ory as w ell. In contrast, if people also organise events w ith respect to non-activity inform ation, then w e should see other kinds of clustering in addition to clustering by activity. For exam ple, people m ight cluster events that share a participant but not an activity. In the high-sim ilarity list, each event cluster shared tw o characteristics (e.g. several instances of presenting an aw ard in H aw aii). Increasing the sim ilarity of events w ithin a cluster should increase the salience of the clusters and thereby increase clustering at recall. Im portantly, how ever, if activities form the dom inant organisation, w ith participant, location, and tim e only organising m em ories subordinately, then this increase in sim ilarity should only occur for clusters that share an activity. E vents not sharing an activity should not be clustered together even though they share tw o characteristics. For exam ple, E vents 1 and 4 in Fig. 1 w ould not be stored together even though they share both a participant (fam ily) and a location (beach). Instead, the increase in sim ilarity should only im prove clustering for events that share an activity (e.g. E vents 2 and 5, w hich share eating as w ell as friends). In contrast, if people cross-classify event m em ories in m ultiple organisations, then w e should observe an increase in clustering even for clusters that do not share an activity. C onsider Events 1 and 4 in Fig 2, w hich cluster under global organisations for both participant and location but do not share an activity. B ecause subjects can retrieve this cluster w hile searching either of tw o organisations, they are m ore likely to retrieve it than to retrieve low sim ilarity clusters, w hich can only be reached from a single organisation. T hus, the crossclassification view predicts a general advantage for high-sim ilarity clusters over low -sim ilarity clusters, regardless of w hether they share an activity. T his experim ent also explored the possibility that people pivot betw een clusters by sw itching organisations over the course of recall. C onsider a possible

9 EVENT ORGANISATION 577 recall of som e fictional events: (1) Paul N ew m an sorted clothes into light and dark piles; (2) C arl Sagan hung the laundry to dry; (3) M argaret Thatcher loaded the w ashing m achine; (4) M argaret Thatcher painted a picture; (5) M argaret T hatcher pruned the tree. In this sequence, the first three events all share an activity (doing laundry), w hereas the last three events all share a participant (M argaret T hatcher). T h e third event serves as the pivot event, shifting the recall organisation from activity to participant. Pivoting has been observed both in the natural recall of events (B arsalou, 1988) and in children s recall of w ord lists (A yres, 1982; C eci & H ow e, 1978; Salatas & Flavell, 1976). In the follow ing experim ent, w e linked pairs of clusters by pivot events to observe w hether subjects w ould use such pivots to shift betw een different organisations of laboratory events. B ecause such pivoting involves event clusters that do not share an activity (e.g. events involving M argaret T hatcher), the strong activity view does not predict it. Method D esign and M aterials. Subjects received the sam e 36 event descriptions in each of tw o study periods and attem pted to free recall the events after each presentation. L earning w as incidental for the first trial and intentional for the second. 2 A low -sim ilarity list and a high-sim ilarity list w ere constructed to im plem ent the sim ilarity m anipulation. E ach event in both lists w as described by four sentences, w ith each sentence describing one characteristic of the event (i.e. activity, participant, location, tim e). Tw o versions of each list w ere constructed. W ithin each version, events w ere ordered random ly, w ith the constraint that events from the sam e cluster w ere not adjacent. A cross the tw o versions, the four sentences describing a given event w ere presented in different random orders, w ith the constraint that each characteristic occurred equally often in every position. T able 1 provides exam ples of event clusters from the low -and high-sim ilarity lists. Low -sim ilarity List. Four different types of clusters w ere form ed, varying in the type of characteristics shared by the events (i.e. activity, participant, loc atio n, or tim e ). T h re e clu ste rs w ere c on stru cte d for ea ch ty pe o f characteristic, resulting in a total of 12 clusters. E ach cluster contained three events. O ne ``pivot event in each cluster also belonged to another ``linked cluster (see T able 1). C onsequently each list contained 30 critical events, w ith the 6 pivot events occurring in 2 clusters each and the 24 rem aining events occurring in 1 cluster each. In addition, each list included 6 buffer events, 3 at the beginning of the list and 3 at the end. 2 N o te th a t p ra c tice is c on fo und e d w ith the tra n sition f ro m in c id e ntal to inte ntion al le a rn ing (a lso in Experim ent 2 ). T hus, conclusions about incidental versus intentional learn ing m ust be draw n w ith c a re. T h e la c k of inte ntio na lity e ffe c ts in o th e r pa ra d ig m s, ho w ev e r, su gg e sts th at it m ay n ot be m u c h of a factor here (e.g. H asher & Z acks, 1979; H yde & Jenkins, 1969).

10 578 LANCASTER AND BARSALOU TABLE 1 Examples of Linked Event Clusters Used in Experiment 1 L ow -sim ilarity C lusters H igh-sim ilarity C lusters T he activity is: bought a boat. The activity is: w ent sw im m ing. T he participant is: D ebra W inger. The participant is: D olly Parton. T he location is: H aw aii. The location is: D allas. T he tim e is: V eteran s D ay. The tim e is: A pril Fool s D ay. T he activity is: bought a boat. The activity is: w ent sw im m ing. T he participant is: A lan A lda. The participant is: D olly Parton. T he location is: D allas. The location is: Italy. T he tim e is: M other s D ay. The tim e is: early M arc h. T he activity is: bought a boat. The activity is: w ent sw im m ing. T he participant is: B arb ara W alters. The participant is: D olly Parton. T he location is: M exico. The location is: B oston. T he tim e is: Fourth of July. The tim e is: L abor D ay. T he activity is: presented an aw ard. The activity is: piloted a plane. T he participant is: B arb ara W alters. The participant is: W alter M ondale. T he location is: France. The location is: B oston. T he tim e is: Easter Sunday. The tim e is: L abor D ay. T he activity is: piloted a plane. The activity is: pro gram m ed a com puter. T he participant is: B arb ara W alters. The participant is: M eryl Streep. T he location is: San D iego. The location is: B oston. T he tim e is: A pril F o o l s D a y. T h e tim e is: L a bo r D a y. F or the low -sim ilarity clusters, the first three events form an activity cluster, and the last three e v e nts fo rm a pa rtic ip an t c lu ste r (the third e v e nt is the p iv ot th a t lin ks th e tw o c lu ste rs ). F o r the h ig h- sim ilarity clusters, the first three even ts fo rm an activity /participant cluster, an d the last three ev ents fo rm a location/tim e cluster (the third event is the pivot that links the tw o clusters). H igh-sim ilarity List. Six different types of clusters w ere form ed, based on the six possible pairin gs of characteristics (i.e. activity±participant, activity± location, activity±tim e, participant±location, participant±tim e, location±tim e). T w o clusters w ere constructed for each type of characteristic pair, resulting in a total of 12 clusters. E ach cluster contained three events. A pivot event in each cluster also belonged to another linked cluster (see T able 1). C onsequently each list contained 30 critical events, along w ith 6 buffer events, 3 at the beginning of the list and 3 at the end. Subjects and P rocedure. Forty undergraduates from E m ory U niversity participated for course credit in groups of one to four. Tw enty subjects received the low -sim ilarity list, and tw enty received the high-sim ilarity list. For each list, half of the subjects received each possible sequence of its tw o versions across the tw o study periods. A ll instruction s an d stim uli w e re prese nte d o n ind ividua l co m p uter w orkstations. W hen ready, subjects received events one at a tim e for seven

11 EVENT ORGANISATION 579 seconds each. A s the four sentences describing an event appeared sim ultaneously on the screen, subjects w ere asked to form an im age of the event as it m ight actually occur. W hen the event w as cleared from the screen, a 7-point scale w as displayed, and subjects rated the event for how easy it w as to im age (i.e. the incidental orienting task). Follow ing presentation of all item s, subjects w ere asked unexpectedly to w rite dow n as m any full or partial events as possible in w hatever order they cam e to m ind. Subjects w ere allow ed as m uch tim e as they needed, usually around five m inutes. W hen finished, subjects signalled the w orkstation and received instructions to study the events again, this tim e w ithout the rating task and expecting a subsequent recall. Follow ing presentation, subjects perform ed a second free recall. Results R ecall. Prelim inary analyses assessed various m easures of subjects ability to recall the events. T he first analysis assessed subjects recall w ithout concern for w hether the recalled events w ere correct or incorrect. B ecause every event recalled by every subject contained at least one characteristic from a presented event, no event w as a com plete fabrication. L ow -sim ilarity subjects recalled a m ean of events on trial 1 and on trial 2, w hereas high-sim ilarity subjects recalled and events on trials 1 and 2, respectively. R ecall increased from the first to the second trial [F (1,32) = , M SE = 4.0 1, P <.0 01]. A lthough sim ilarity had no effect [F (1,3 2) < 1, M SE = ], it interacted w ith trial [F (1,3 2) = 4.0 4, M SE = 4.0 1, P <.0 5]. H igh-sim ilarity subjects im proved slightly m ore over trials than low -sim ilarity subjects. A second analysis assessed how frequently subjects recalled different types of event characteristics. D id subjects vary in how often they recalled activities, participants, locations, and tim es from the events? T he num ber of tim es a subject recalled presented event characteristics w as scored, independent of w he ther each recalled charac teristic w as accom pan ied by o ther co rrect characteristics from an actual event. Subjects hardly ever recalled an event characteristic that w as not presented. In the few cases they did, it w as alw ays a generalisation of a presented characteristic and w as not included in this analysis. If a subject recalled a particular characteristic m ore tim es than it actually occurred, each occurrence w as counted once (this happened rarely). T able 2 provides the relevant m eans. A ga in fre qu e nc y o f rec all inc rea sed a cro ss tria ls [F (1,32 ) = M SE = , P <.0 01]. C haracteristics varied in how w ell they w ere recalled [F(3,9 6) = , M SE = , P <.0 01]. C ontrasts found that participants (16.76) and activities (16.0 6) w ere recalled equally often, both being recalled m ore often than locations (11.38), w hich w ere recalled m ore often than tim es (6.2 8). C haracteristic also interacted w ith trial [F(3,9 6) = 17.58, M SE = 2.3 5, P <.0 01]. R ecall im proved less across trials for tim es than for participants, activities, and locations. A gain high sim ilarity recall (13.5 4) w as not reliably

12 580 LANCASTER AND BARSALOU TABLE 2 Mean Frequency of Event Characteristics Recalled in Experiment 1 C on d ition T rial L ow -sim ilarity H igh-sim ilarity P articipant A c tivity L oc a tio n T im e T he total num ber that could have been recalled fo r each type of characteristic w as 36. better than low sim ilarity recall (11.69) [F (1,32) = 1.58, M SE = , P =.2 1]. T his general pattern of results suggests that participants and activities w ere m ore central to subjects conceptualisations of the events than w ere the locations and tim es in w hich they occurred. A third analysis assessed subjects correct recall of the events. E ach recalled event w as scored as correct if it contained three of the four characteristics from a presented event. In all cases, three correctly recalled characteristics uniquely specified a presented event. W hereas the average num ber of total events recalled by subjects w as 18.50, the average num ber of correctly recalled events w as C aution should be taken w hen interpreting events that w ere recalled but not correct by this criterion. B ecause subjects m ay have correctly recalled part of an event, but not enough to satisfy the three-characteristic criterion, the event m ay not constitute an actual intrusion. Separate A N O V A s w ere perform ed for the low -and high-sim ilarity conditions. T able 3 provides the average frequency of total events recalled per subject for each cluster type. 3 In the low -sim ilarity condition, the average num ber of the nine total events recalled correctly per cluster type increased from the first trial (1.2 9) to the second trial (3.1 0) [F (1,1 6) = , M SE = 2.27, P <.0 01]. C luster type had no effect for low -sim ilarity clusters [F (3,48) = 1.56, M SE = 0.9 7, 3 R e c a ll th a t the re w e re thre e cl uste rs fo r e a c h o f the fo ur ty pe s in th e lo w - sim ilarity c on ditio n versus tw o clusters for each of the six types in the high-sim ilarity condition, w ith every cluster containing three events. Thus, there w ere 36 critical even ts that subjects could have recalled in each sim ila rity c o nd ition. B e c a use 6 w e re p iv ot e ve n ts, e a c h c o un ting on c e fo r e a c h of tw o c lu ste rs, th e re w ere actually only 30 critical events in each list.

13 EVENT ORGANISATION 581 TABLE 3 Average Total Frequency of Correctly Recalled Events for Each Cluster Type in Experiment 1 C luster Type Trial 1 Trial 2 Low-sim ilarity C ondition P a rtic ip a nt A ctivity L o c a tio n Tim e H igh-sim ilarity C ondition Participant/A ctivity P a rtic ip a nt/l oc a tion Participant/Tim e A ctivity/location A ctivity/tim e Location/Tim e A n event w as coded as corre ctly recalled if a subject recalled at least three of its presented characteristics. Subjects received three clusters of each type in the low -sim ilarity condition an d tw o c lu ste rs of e ac h ty pe in th e high -s im ila rity c on ditio n, w ith e a c h c luste r co n ta in in g th re e e v en ts. T h us, the m axim um recall fo r each average w as nine events in the low -sim ilarity condition and six events in the high -sim ilarity condition. P =.2 1]. In the high-sim ilarity condition, the average num ber of the six total events recalled correctly per cluster type increased from the first trial (1.3 2) to the second trial (2.8 4) [F(1,1 6) = 72.04, M SE = 1.7 9, P <.001] and varied across cluster type [F(5,8 0) = 5.4 2, M SE = 1.30, P <.0 1]. C lustering. T he prim ary analyses to follow assessed the organisation of subjects protocols. A cluster w as defined as any contiguous sequence of tw o or three recalled events that shared a com m on value for at least one event characteristic, such as: B rooke S hields threw a party in S an D iego on S t. P atrick s D ay. B rooke S hields threw a party in F rance. or: G eraldine F erraro w ent on a diet, a v acatio n and sold a farm. N ote that subjects often om itted event characteristics, som etim es because they could not rem em ber them (e.g. the tim e for the second Shields event), and

14 582 LANCASTER AND BARSALOU som etim es because of ellipsis (the participant in the second and third Ferraro events). W hen ellipsis w as obvious, subjects w ere credited w ith recalling the ellipsed inform ation. C luster analyses w ere perform ed on all recalled events, regardless of w hether they w ere scored as correct or not (as described earlier). W e w ere prim arily interested in how subjects organised w hatever events they thought they had seen, and the presence of im perfectly recalled events does not interfere w ith observing such organisation. If w e had elim inated incorrect events from these analyses, w e w ould have created gaps in subjects recall sequences that w ould have been difficult to interpret. B y including all events recalled, w e m aintained the overall conceptual organisation of each subject s recall. Furtherm ore, these clusters are also based on inferred event characteristics. For exam ple, if the location for an event w as not m entioned, but if the event s other characteristics uniquely identified it as belonging to a particular location cluster, the event w as counted as belonging to it. Finally, w hen buffer events w ere recalled, they never counted tow ards a cluster, given each had no shared event characteristics, but they could break up events from a cluster surrounding them. Fo r exam ple, if a buffer event occurred betw een tw o events from a location cluster, these tw o events w ere not counted as a cluster. L ow -sim ilarity subjects form ed an average of clusters on trial 1 and clusters on trial 2, w hereas high-sim ilarity subjects form ed an average of clusters on trial 1 and clusters on trial 2. 4 A s predicted, m ore clustering occurred for high-sim ilarity subjects (3.7 5) than for low -sim ilarity subjects (1.6 0) [F (1,3 8) = , M SE = 7.3 7, P <.0 1]. C lustering increased from trial 1 (1.7 0) to trial 2 (3.65) [F (1,38) = 41.94, M SE = 1.8 1, P <.001], w ith sim ilarity and trial interacting m arginally [F (1,3 8) = 3.3 4, M SE = 1.8 1, P <.1 0]. T he increase in clustering across trials w as greater for high-sim ilarity subjects than for low -sim ilarity subjects. T he next analysis assessed the relative rate at w hich subjects produced clusters of different types. T he strong activity view predicts that subjects should never have form ed a non-activity cluster. The w eak activity view acknow ledges the production of non-activity clusters but predicts that activity clusters should occur m ost frequently. O ther cross-classification view s predict either that no organisation should dom inate, or that som e non-activity organisation could dom inate. T able 4 presents the average frequency of clusters that a subject produced for each organisational type. In the low -sim ilarity condition, cluster type varied, w ith subjects producing m ore activity and participant clusters than location and tim e clusters [F (3,5 7) = 4.5 1, M SE = 0.3 4, P <.0 1]. A ctivity clustering w as no t significantly higher than participant clustering on either 4 O n a give n tria l, a su bjec t c o uld h a ve fo r m e d a m a x im u m o f 12 c lu ste rs, 3 fo r e a c h o f 4 org an isations in the low -sim ilarity condition, or 2 fo r each of 6 organisations in the high -sim ilarity c o nd itio n.

15 EVENT ORGANISATION 583 TABLE 4 Mean Frequency (ARC Score) of Clusters in Experiment 1 O rganisation Trial Participant Activity Location Tim e Low-sim ilarity C ondition (0.0 7) 0.50 (0.0 5) 0.15 (0.0 8) 0.05 (0.0 8) (0.2 3) 0.80 (0.2 7) 0.45 (0.0 2) 0.35 (0.0 6) O rganisation P articipant/ P a rtic ip an t/ P a rtic ip a nt/ A c tivity / A c tivi ty / L o c atio n/ T rial A ct iv ity L oc a tio n T im e L o c ation T im e T im e H igh-sim ilarity C ondition (0.51 ) (0.36 ) (0.21 ) (0.2 6) (0.2 5) 0.05 (0.0 0) (0.80 ) (0.46 ) (0.44 ) (0.4 0) (0.3 0) 0.40 (0.2 8) C o llap se d O rg a nisa tion a Trial Participant Activity Location Tim e (0.3 6) 1.75 (0.3 4) 1.00 (0.2 1) 0.75 (0.1 5) (0.5 7) 2.95 (0.5 0) 1.90 (0.3 8) 1.95 (0.3 4) T he m axim um fre quency fo r m ean cluster fre quency is 3 in the low sim ilarity condition and 2 in the high sim ilarity co ndition. a S e e the tex t fo r a de sc rip tion o f h ow o rg a n isa tio ns w e re co lla pse d. trial. Furtherm ore, cluster type did not interact w ith trial [F(3,5 7) =.3 3, M SE = 0.3 0]. T hese results reject the strong activity view, given that subjects produced non-activity clusters. T hese results also reject the w eak activity view, given that activity clustering did not dom inate all other types (i.e. participant clustering w as equally frequent). T he results for the high-sim ilarity condition in T able 4 yield sim ilar conclusions. A gain, cluster type varied, w ith participant±activity clusters being m ost com m on, location±tim e clusters being least com m on, and the other four cluster types being interm ediate [F (5,95) = , M SE = 0.37, P <.0 01]. A gain, cluster type did not interact w ith trial [F (5,9 5) = 1.62, M SE = 0.2 7]. A s in the low -sim ilarity condition, subjects form ed clusters that did not share activities, contrary to the strong activity view. T o estim ate the relative im portance of each organisational type in the high sim ilarity co nd itio n th e th re e freq ue nc ie s fo r a ll clusters inv olving a characteristic w ere sum m ed. For exam ple, the sum of the participant±activity, participant±location, and participant±tim e frequencies for trial 1 represents the relative im portance of participant organisation on trial 1. T hese estim ates are show n at the bottom of T able 4. B ecause each uncollapsed m ean contributes to tw o collapsed m eans, the lack of independence betw een the collapsed m eans m akes perform ing statistical tests questionable. H ow ever, an exam ination of the

16 584 LANCASTER AND BARSALOU collapsed m eans suggests that activity and participant organisation w ere of roughly equal im portance on both trials and m ore im portant than location and tim e organisation. T his result disconfirm s the w eak activity view, w hich assum es that activity organisation should dom inate all other types. A s w e have seen, subjects form ed non-activity clusters. T he follow ing analysis explores this finding further. R ecall that subjects form ed m ore clusters in the high- sim ilarity condition than in the low -sim ilarity condition. A ccording to the strong activity view, this sim ilarity effect should only involve activity clusters. A s describ ed in the introduction to this experim ent, increasing the sim ilarity of the events in a cluster should only increase clustering w hen these events already share an activity. T hus, adding a com m on participant to several events that share a location but not an activity should not increase the likelihood of clustering them at recall, because they are distributed across different activity organisations. T o assess this hypothesis, w e com puted the average proportion of the possible non-activity clusters produced per subject (i.e. clusters sharing only participant, location, and/or tim e). Proportions w ere used as the dependent m easure, because the num ber of possible non-activity clusters differed in the low -(9) and high-(6) sim ilarity conditions. T he probability of form ing a non-activity clustering increased from trial 1 to trial 2 [F(1,3 8) = , M SE = 0.09 arcsin units, P <.0 01]. Low -sim ilarity subjects form ed of the possible non-activity clusters on trial 1 and of the possible non-activity clusters on trial 2. T he proportions for high-sim ilarity subjects w ere on trial 1 and 0.34 on trial 2. M ost im portantly, the overall probability of form ing a non-activity cluster w as higher for high-sim ilarity subjects (.2 3) than for low -sim ilarity subjects (.1 1), indicating that non-activity clusters contributed to the sim ilarity effect [F (1,3 8) = , M SE = arcsin units, P <.0 1]. C ontrary to the strong activity view, higher event sim ilarity increased clustering, even w hen the events in a cluster did not share an activity. T rial and cluster type did not interact [F (1,3 8) = 1.7 0, M SE = arcsin units]. A R C A nalysis. T he average frequency of clusters in T able 4 is not corrected for differential recall of events from the various cluster types. T o the extent that som e cluster types are better recalled than others, they have a greater probability of producing clusters by chance. T o correct for this possibility, the A ssociated R atio of C lustering (A R C ) w as applied to the protocols (R oenker, T hom pson, & B row n, 1971). W hen clustering occurs at chance levels, A R C scores approxim ate 0; w hen clustering is perfect, A R C scores approxim ate 1. T able 4 presents A R C scores by condition in parentheses. In the low -sim ilarity condition, there w as no overall effect of cluster type [F (3,5 7) = 1.14, M SE = ]. C lustering increased from trial 1 to trial 2 ( v s ), b u t t h i s d i f f e r e n c e o n l y a p p r o a c h e d s i g n i f i c a n c e [F (1,1 9) = 2.14, M SE = , P =.1 6]. C luster type and trial did not interact [F (3,5 7) = 1.48, M SE = ]. O n trial 1, clustering w as significantly greater than zero [t(19) = 2.0 9, SE = , P <.0 5], but cluster types did not differ.

17 EVENT ORGANISATION 585 O n trial 2, clusters for participants and activities w ere m arginally m ore frequent than clusters for locations and tim es. N otably, activity clustering failed to dom inate all other form s of clustering, contrary to the strong and w eak activity view s. C lustering increased reliably from the low -sim ilarity condition (0.1 1) to the high-sim ilarity condition (0.36) [F (1,3 8) = 18.26, M SE = 0.0 7, P <.0 01]. W ithin the high-sim ilarity condition, clustering increased from trial 1 (0.2 6) to trial 2 (0.4 5) [F (1,1 9) = , M SE = 0.1 0, P <.001] and varied across cluster types [F(5,9 5) = 5.43, M SE = 0.2 1, P <.0 01]. T rial and cluster type did not interact [F(5,9 5) =.5 2, M SE = 0.1 8]. Participant±activity clustering w as m ost prevalent, follow ed by clusters that shared either participants or locations. W hen clustering principles w ere collapsed (T able 4), activity clustering failed to dom inate all other form s of clustering, being slightly less than participant clustering. Together, the results for cluster frequency and A R C scores cast doub t on the strong and w eak activity view s. In contrast to the strong activity view, subjects form ed clusters of events that did not share activities. In contrast to the w eak activity view, activity clusters did not dom inate all other types, given that participant clusters occurred as frequently as activity clusters. In general, these results support the conclusion that subjects cross-classified events into m ultiple organisations at the global level. C luster Length. T he m ean length (i.e. num ber of events) per cluster w as com puted for each characteristic in the low -sim ilarity condition and each pair of characteristics in the high-sim ilarity condition. Each m ean length w as com puted across only those subjects producing clusters of that type. H igh-sim ilarity subjects produced longer clusters than low -sim ilarity subjects on both trial 1 [2.2 4 vs ; t(64) = 8.4 3, SE = 0.07, P <.0 01] and trial 2 [2.35 vs. 2.18; t(138) = 2.3 6, SE = 0.0 8, P <.0 5]. C lusters w ere longer on trial 2 than on trial 1 in the high-sim ilarity condition [t(143) = 3.96, SE = 0.07, P <.0 01] but not in the low -sim ilarity con dition [t(59) = 1.2 1, SE = 0.1 0]. A ctivity±participant and a ctiv ity ±loc ation clusters w e re b o th lo ng e r th a n ac tiv ity±tim e clusters [t(69) = 2.3 6, SE = 0.1 2, P <.05; t(45) = 2.36, SE = 0.12, P <.0 5]. O ther than these effects, there w as little variation in cluster length across cluster types. L ancaster (1985) provides further details about the findings on cluster length. P ivoting. Pivot events w ere defined as a recalled event that ended one cluster in a subject s protocol but sim ultaneously began another cluster. For exam ple, the follow ing protocol segm ent illustrates a pivot from a participant± location cluster to an activity±location cluster: G eraldine F erraro program m ed a com puter in N ew O rleans. G eraldine F erraro started a diet in N ew O rleans. W oody A llen started a diet in V erm ont. D olly Parton started a diet in V erm ont.

18 586 LANCASTER AND BARSALOU TABLE 5 Type (Frequency) of Pivots in Experiment 1 C o nd ition Trial L ow -sim ilarity H igh-sim ilarity 1 P A (1 ) P A L (1 ) A P (1 ) P L A L (1 ) P A L (1 ) 2 A P (2 ) L T P A (2 ) P L (1 ) P A (1 ) L P A (1 ) A P L (1 ) A L T (1 ) P L A (1 ) P L T (1 ) x y m eans that a subject pivoted from a cluster of type x to a cluster of type y, w here P, A, L, and T refer to participant, activity, location, and tim e, re spe c tive ly. T able 5 show s the num ber and types of pivots that subjects produced. T he characteristics defining each pivot are indicated by the single letters, A, P, L, and T, w hich represent activity, participant, location, and tim e clusters, respectively. T he arrow s represent the transition from the first cluster in each pivot to the second. N ote that high-sim ilarity subjects som etim es constructed clusters based on one characteristic instead of tw o. Pivoting occurred m ore frequently on trial 2 (11 occurrences) than on trial 1 (5 occurrences). T he pattern across both sim ilarity conditions w as for pivoting to occur only in the presence of clustering by participant or activity, given that 100% of the piv ots involved eith er or both of these characteristics. O verall, neith er participant or activity dom inated, w ith 94% of the pivots involving participant and 88% involving activity. A cross pivots, 56% w ere a transition from a cluster sharing at least a participant or activity to a cluster sharing at least an activity or participant; 31% w ere the result of pivoting from a cluster sharing at least a participant or activity to a cluster sharing at least location. Discussion T hese results contradict the strong activity view of event organisation, w hich proposes that subjects should never cluster events that do not share an activity. O n the contrary, subjects form ed clusters that did not share an activity but that shared a participant, location, or tim e. Furtherm ore, non-activity clusters contributed to the sim ilarity effect in clustering. A s the sim ilarity of events w ithin a cluster increased, the probability of clustering them at recall increased as w ell, even for clusters that did not share an activity. Finally, subjects pivoted