Abstract

Humans are capable of storing and retrieving sequences of complex structured events. Here we report a study in which we establish the psychological structure of event knowledge and then use parametric event-related functional magnetic resonance imaging to identify its neural correlates. We demonstrate that event knowledge is organized along dissociable dimensions that are reflected in distinctive patterns of neural activation: social valence (amygdala and right orbitofrontal cortex), experience (medial prefrontal cortex) and engagement (left orbitofrontal cortex). Our study affirms the importance and uniqueness of the human prefrontal cortex in representing event knowledge.

Introduction

People can parse, store and retrieve routine individual events, e.g. ‘preparing coffee’, that compose a hierarchically structured sequence of events, e.g. ‘getting up in the morning’. Such event knowledge facilitates performance of routine and adaptive behaviors (Schank and Abelson, 1977; Wood and Grafman, 2003). Event knowledge contains information about activities of daily living; for example, an event knowledge representation of the activity of ‘going out to dinner’ would consist of choosing a restaurant, making a reservation, reading the menu, ordering the food, paying the check, etc. It has been proposed that event knowledge is represented in the prefrontal cortex (PFC) (Burnod, 1991; Fuster, 1997; Grafman, 2002; Wood and Grafman, 2003); however, this viewpoint is not without controversy (Wood and Grafman, 2003). Although there is some evidence that the PFC is implicated in storage of event knowledge (Partiot et al., 1995, 1996; Sirigu et al., 1995b, 1996; Crozier et al., 1999; Zacks et al., 2001; Wood et al., 2003), little is currently known regarding how that information is organized. The present study directly addresses this issue in two experiments: (i) identification of the psychological structure of event knowledge using multidimensional scaling (MDS); and (ii) identification of the pattern of neural activity associated with the primary factors of this structure using functional magnetic resonance imaging (fMRI).

MDS is a qualitative analysis technique that has been usefully applied to a range of data in order to explain the underlying structure of the appropriate representations (Kruskal and Wish, 1978; Martin and Caramazza, 1980; Schiffman et al., 1981; Forgas, 1982; Halberstadt and Niedenthal, 1997; Edelman, 1998; Taylor et al., 1999). MDS is applied to similarity ratings of pairs of stimuli; these ratings form a distance matrix. The purpose of MDS is to model the data using the smallest number of dimensions required to preserve the distances between the pairs. The resulting model gives a visuospatial ‘picture’ of the structure of the stimuli, in which each stimulus is described by coordinates (values) on a number of dimensions. These dimensions can then be identified by regressing the dimension coordinates against ratings on pre-selected variables-of-interest. The variables are selected based on extant theory and previous research. MDS is an exploratory data analysis procedure that can give reliable results with relatively few participants. Forgas previously applied MDS to the structure of knowledge regarding common social activities (Forgas, 1976, 1978, 1982). He used sample sizes of 3, 5 and 8 subjects (Forgas, 1978) and demonstrated that these representations are organized along three dimensions: (i) level of involvement in the situation, (ii) knowledge of the rules applicable to the situation and (iii) its emotional valence. However, this research was limited to common social activities and did not address potential category-specific differences between social and non-social behaviors.

In the present study, we applied MDS to similarity ratings of pairs of events (e.g. ‘reading the menu’ paired with ‘get the detergent’ or ‘order the food’). We included events from social and non-social activities. The variables-of-interest for the regression analyses were selected to be:

An advantage of MDS analysis is that it makes few assumptions about how the data are modeled and, as such, the psychological structure of event knowledge may implicate all or none of our selected variables. The individual variables-of-interest may not map onto the dimensions of the psychological structure with a one-to-one correspondence, i.e. dimensions may comprise combinations of variables. In a novel approach, we used fMRI (Moonen and Bandettini, 2000) to identify the neural correlates of these event knowledge representations using the dimensions established in the MDS phase of the study. Each event was associated with three values from the MDS analysis (one for each dimension). We used parametric analyses of event-related fMRI data in order to establish which brain regions exhibited activation that covaried with the values for each of the dimensions identified in the MDS experiment. As our interest is in determining the relationship between the psychological and neural representations of event knowledge, the novel combination of the MDS and fMRI approaches enables us to address the question of how the psychological structure of event knowledge maps onto neural networks while limiting the assumptions that must be made about the involvement of specific variables.

Experiment 1: Multidimensional Scaling Analysis of Event Knowledge

In experiment 1, we collected similarity ratings of pairs of events and applied MDS analysis to these ratings in order to elucidate the psychological structure of event knowledge.

Materials and Methods

Participants

Participants were 24 native English-speakers. Each group consisted of six individuals: younger males and females (21–27 years, mean 24.0 and 23.0 years) and older males and females (48–65 years, mean 57.2 and 57.8 years). All participants gave informed consent to a protocol that had been approved by the Institutional Review Board.

Stimuli, Presentation Conditions and Procedure

The stimuli were events from a normative study of event knowledge (Rosen et al., 2003), with four social (e.g. going out to dinner) and four non-social (e.g. doing the laundry) activities and 12 events for each activity (96 events in total; see Appendix). Social activities were defined as those that involved more than one person, whereas non-social activities were defined as those that involve only one person. The activities were selected such that the frequencies and emotional valences of the social and non-social activities were similar. Each event was paired with every event (including itself) and presented to participants for similarity ratings on a 1–7 scale where 1 was not similar at all and 7 was extremely similar. Participants also rated each event for age of acquisition, frequency of performance, socialness, commonality, emotional valence, level of involvement and rule knowledge on a 1–7 scale. The presentation order of stimuli for each set of ratings was fully randomized for each subject; further, the order of presentation of first and second items of each pair for similarity ratings was counterbalanced across participants. Participants completed the study over three testing sessions of 2 h each (seven participants needed four and one needed five sessions). The similarity ratings were completed prior to the individual event ratings on the variables-of-interest. The stimuli were presented in a Microsoft Excel worksheet and participants typed their responses onto the spreadsheet in the cell next to the event or pair of events. Each rating type was presented on a separate spreadsheet and contained no ratings of other participants.

Data Analysis

Individual differences MDS analysis was performed using SPSS (version 8.0 for Macintosh). Participants were entered into the analysis as separate sources and the dimensions weights for each individual computed. The resulting dimension coordinates were entered into linear regression analyses. Where these resulted in multiple models, the selected model was that which gave the highest value of R2 with a non-significant improvement (P > 0.05) on addition of further variables.

Results

Individual differences MDS was applied to the similarity ratings. A three-dimensional model was selected on the basis of stress (goodness-of-fit) values, where stress values of < 0.1 indicate a good fit (Kruskal and Wish, 1978) and the optimal number of dimensions is indicated by an elbow in the plot of stress (Kruskal and Wish, 1978; Schiffman et al., 1981). Both the three- and four-dimensional solutions appear to be appropriate; we selected the three-dimensional model for reasons of parsimony.

Neither gender nor age affected the importance of each dimension in the MDS model, Fs(2,40) < 2, NS. Therefore, linear regression analyses were performed for the combined sample of 24 participants.

Dimension 1 was best explained by level of involvement and social relatedness; R2 = 0.22, F(2,93) = 13.47, P < 0.001. Dimension values were positively correlated with level of involvement, r96 = 0.39, P < 0.001, which was positively correlated with social relatedness, r96 = 0.47, P < 0.001. Dimension 1 is an engagement component, with highly personal activities requiring a high degree of attention (e.g. ‘shave with a razor’ and ‘take a shower’) and impersonal activities requiring less attention (e.g. ‘find a ball’ and ‘start bowling’) forming the extremes of the continuum.

Dimension 2 was best explained by the variables of emotional valence and social relatedness; R2 = 0.55, F(2,93) = 57.75, P < 0.001. Dimension values were negatively correlated with social relatedness, r96 = −0.69, P < 0.001, and negatively correlated with valence, r96 = −0.24, P = 0.021. Dimension 2 is a social valence component, with negative non-social events tending to score higher in this dimension, and positive social events tending to score lower. The events ‘leave the child's bedroom’ and ‘read the story’ scored highest on Dimension 2, while the events ‘pay the check’ and ‘wait to be seated’ scored lowest.

Dimension 3 was best explained by the variables of rule knowledge, commonality, and frequency; R2 = 0.54, F(3,92) = 35.31, P < 0.001. Dimension values were positively correlated with rule knowledge, r96 = 0.61, P < 0.001, and frequency, r96 = 0.57, P < 0.001, and negatively correlated with commonality, r96 = −0.23, P = 0.025. Dimension 3 is an experience component, with frequently performed, well-known activities (e.g. ‘turn off the alarm’ and ‘read the newspaper’) and infrequently performed activities about which we have less knowledge (e.g. ‘choose room’ and ‘choose the paint type and color’) forming the extremes of the continuum.

Discussion

MDS is a qualitative analysis technique that has been usefully applied to a range of data in order to explain the underlying structure of the appropriate representations (Kruskal and Wish, 1978; Forgas, 1982; Schiffman et al., 1981; Halberstadt and Niedenthal, 1997; Edelman, 1998). We applied individual differences MDS to similarity ratings of pairs of social and non-social events (e.g. ‘reading the menu’ paired with ‘get the detergent’ or ‘order the food’). A three-dimensional model was selected on the basis of goodness-of-fit. We interpreted the dimensions revealed by MDS as reflecting (i) engagement, (ii) social valence and (iii) experience.

Previous research by Forgas exploring the internal structure of daily activities focused on common social activities and reported a three-dimensional structure of level of involvement, rule knowledge and emotional valence (Forgas, 1976, 1978, 1982). The present findings are broadly consistent with Forgas' research and extend his findings by exploring social and non-social events and by focusing on the component events of the activities rather than hierarchical activity headings, e.g. the events of ‘adding detergent’ and ‘sorting clothes’ rather than the hierarchical activity of ‘doing the laundry’. In addition, the dimensions we obtained appear to be more complex than those of Forgas, with no dimension mapping onto a single variable. Rather, each of Forgas' dimensions was evident in combination with other variables in our study. It is important to note that Forgas' study used very different events from the present study and that we nonetheless broadly replicated his findings. This suggests that our findings may generalize to event knowledge as a whole and are not restricted to the set of events used in the present study.

Younger and older participants participated in the study, since if age-of-acquisition was implicated in the dimensions, we could then determine if there was a correlation between age-of-acquisition and current age. However as age-of-acquisition was not implicated in any dimension, we collapsed our analyses across participants. The lack of significance for age-of-acquisition suggests that established effects of age-of-acquisition on semantic knowledge (Snodgrass and Yuditsky, 1996; Morrison and Ellis, 2000) do not necessarily extend to event knowledge.

Experiment 2: Neural Correlates of Event Knowledge

Given the empirical findings of experiment 1, we predicted that values on the experience dimension would be associated with medial PFC based on theory and previous findings that medial PFC regions store predictable event sequences (Koechlin et al., 2000, 2002; Wood and Grafman, 2003). Values on the social valence and engagement dimensions were expected to be associated with orbitofrontal cortex given its proposed role in social and emotional processing (Blair and Cipolotti, 2000; Moll et al., 2002a), stereotypes (Milne and Grafman, 2001), aggression (Raleigh et al., 1979; Grafman et al., 1996; Blair and Cipolotti, 2000), moral reasoning (Greene et al., 2001; Moll et al., 2002b) and emotions (e.g. Rolls, 1996, 1999; Hornak et al., 2003; Streit et al., 2003). Finally, values on the social valence dimension were predicted to be associated with amygdala activity due its involvement in processing negatively valenced stimuli (e.g. Lane et al., 1997; Tabert et al., 2001).

Materials and Methods

Participants

Participants were 18 right-handed (Oldfield, 1971), native English-speakers (10 male, 8 female, 22–39 years, mean age 28.1 years). These participants had not participated in experiment 1. No participants reported a history of neurological or psychiatric problems and the fMRI participants had a normal neurological examination by an NINDS neurologist during the 12 months prior to the experiment. All participants gave informed consent to a protocol that had been approved by the Institutional Review Board.

Stimuli, Presentation Conditions and Procedure

The stimuli were the same events as in experiment 1. There were two tasks. In the category task, participants decided whether an event was social (a social event involves more than one person). The selection of socialness as the categorization task was intended to ensure that the subject focused on the stimuli as events, rather than simply as phrases or groups of words. In the font task, participants decided whether the event was written in the target font (Palatino or Helvetica; the target was counterbalanced across participants). Both tasks required simple yes/no responses. The font task was included in case there was insufficient power to detect activations in the parametric analyses; however, there was sufficient power using parametric analysis, so the font condition was not used as a subtraction condition. There were four runs in total. Each event was presented twice (once with each task); repetition of the events was separated by at least one run and the repetition order (category or font task first) was counterbalanced across participants. The events were randomly assigned to one of the four runs, with the constraint that equal numbers of events in the category and font tasks were presented in each run.

Each trial consisted of a slide that stated the task (CATEGORY or FONT) and the event printed below (e.g. ‘order the food’, ‘add the detergent’). Stimulus presentation was event-related and jittered, with trials randomly assigned to one of four stimulus presentation times (3, 4.33, 5.67, 7 s). Each presentation time includes a 250 ms blank inter-trial interval. Random assignment of jitter length to events was constrained by the need to ensure similar distribution of the trial lengths across the tasks. Jittering the stimulus presentation around a mean length increases the number of time-points over which the hemodynamic response is sampled and ensure sufficient sampling points to allow estimation of the shape and duration of the hemodynamic response (Dale and Buckner, 1997; Dale, 1999; Miezin et al., 2000).

High-resolution anatomical images were acquired for the purposes of data presentation with a 1.5 T GE scanner (Milwaukee, Wisconsin) using a 3D SPGR sequence to obtain 124 contiguous slices (axial acquisition, slice thickness = 1.5 mm, in-plane resolution = 0.9375 × 0.9375 mm2). Functional images were acquired using a 2D gradient echo, EPI sequence to obtain 22 contiguous slices (axial acquisition, TR = 2 s, TE = 40 ms, flip angle = 90°, FOV = 24 cm, slice thickness = 6 mm, in-plane resolution = 3.75 × 3.75mm2). Head motion was restricted using a head strap and foam pads placed around the subject's head. Visual stimuli were back-projected onto a screen that was viewed in a mirror attached to the head coil.

Data Analysis

fMRI data preprocessing was carried out as described elsewhere (Wood et al., 2003) with the exception that data were spatially smoothed with an 8 mm FWHM isotropic Gaussian kernel (Friston et al., 1995). Statistical analyses were carried out in SPM99. The trials of each condition (category and font) and subject were modeled using a canonical hemodynamic response function with parametric modulation of the category condition using the dimension coordinates for each dimension. Parametric modulation identifies those regions of the brain that exhibit activation that varies with the variable-of-interest. Separate parametric analyses were performed for each dimension. Data were globally scaled at the individual subject level of analysis to allow comparison of images from different individuals at the group level of analysis. Data were temporally smoothed in SPM99 using the standard HRF filter to remove effects due to physiological noise.

Contrast images for each comparison or effect were entered into second-level random effects analyses — these take into account inter-subject variability, eliminate the possibility of one subject skewing the results, and allow inferences to be made regarding the population in general (Friston et al., 1999). One-sample t-tests were used at the group analysis level to determine voxel-wise t-statistics for each condition. The intention of imaging data analysis is to identify true task-related brain activation and, therefore, multiple comparisons must be taken into account when reporting and interpreting imaging data. Correction for multiple comparisons in PFC and limbic regions (for which we had a priori hypotheses) was carried out using an uncorrected P-value of 0.01 and an extent threshold of 20 — this corresponds to a per voxel false positive probability (Forman et al., 1995) of <0.000004. This is an established method of dealing with multiple comparisons (Konishi et al., 1999; Poldrack et al., 1999; Wagner et al., 2001; Wood et al., 2003). The MNI coordinates were transformed into Talairach stereotactic space (Talairach and Tournoux, 1988; Duncan et al., 2000) and approximate Brodmann areas of the activations were determined. Whole-brain analysis using corrected P-values (corrected P < 0.05) gave no additional regions of activation.

fMRI Results

The experience dimension modulated activation in the right medial PFC while the engagement dimension modulated activation in the left orbitofrontal cortex (see Table 1 and Fig. 1a,b).

Figure 1.

Activation associated with each dimension: (a) medial PFC activation associated with the experience dimension, (b) left orbitofrontal activation associated with the engagement dimension, and (c) amygdala activation associated with the social valence dimension.

Figure 1.

Activation associated with each dimension: (a) medial PFC activation associated with the experience dimension, (b) left orbitofrontal activation associated with the engagement dimension, and (c) amygdala activation associated with the social valence dimension.

Table 1

Anatomical localization of the activation associated with each dimension

Anatomical localization of the peak of the cluster Cluster size Talairach coordinates
 
  t-score P-value 

 

 
x
 
y
 
z
 

 

 
Social valence       
R amygdala 214 24 −12 −13 4.15 < 0.001 
L anterior cingulate, BA 32 162 −24 32 13 3.63 0.001 
L amygdala 147 −32 −8 −10 3.95 0.001 
R premotor cortex, BA 6 26 28 −20 60 3.40 0.002 
L premotor cortex, BA 6 23 −8 −17 56 3.42 0.002 
R orbitofrontal cortex, BA 11/47
 
21
 
44
 
35
 
−8
 
3.63
 
0.001
 
Experience       
R medial PFC, BA 10
 
35
 
4
 
54
 
−9
 
3.60
 
0.001
 
Engagement       
L orbitofrontal cortex, BA 47
 
40
 
−24
 
15
 
−14
 
3.63
 
0.001
 
Anatomical localization of the peak of the cluster Cluster size Talairach coordinates
 
  t-score P-value 

 

 
x
 
y
 
z
 

 

 
Social valence       
R amygdala 214 24 −12 −13 4.15 < 0.001 
L anterior cingulate, BA 32 162 −24 32 13 3.63 0.001 
L amygdala 147 −32 −8 −10 3.95 0.001 
R premotor cortex, BA 6 26 28 −20 60 3.40 0.002 
L premotor cortex, BA 6 23 −8 −17 56 3.42 0.002 
R orbitofrontal cortex, BA 11/47
 
21
 
44
 
35
 
−8
 
3.63
 
0.001
 
Experience       
R medial PFC, BA 10
 
35
 
4
 
54
 
−9
 
3.60
 
0.001
 
Engagement       
L orbitofrontal cortex, BA 47
 
40
 
−24
 
15
 
−14
 
3.63
 
0.001
 

R, right; L, left.

The social valence (more negative, non-social events) dimension modulated activation in a more distributed network of regions comprising bilateral amygdala, right orbitofrontal cortex, left anterior cingulate and bilateral premotor cortex (see Table 1 and Fig. 1). The amygdala and orbitofrontal cortex activation was consistent with our predictions. Events that were high on this dimension included non-social events. We have previously reported activation of the anterior cingulate and premotor cortex when subjects were processing non-social event knowledge (Wood et al., 2003) and the present results are consistent with our previous findings.

Mean response latencies to make a category decision about each event were computed by averaging across subjects. Correlations between these mean response latencies and the dimension values for these events were not significant, r96s < 0.1, NS. As response latency provides an index of task difficulty, these results indicate that the dimension values, and by implication the observed PFC activations, are not the result of task difficulty.

Discussion

Having established the psychological structure of event knowledge in experiment 1, fMRI was then used to identify the locus of these event knowledge dimensions in the brain. The fMRI study demonstrated that event knowledge is stored in a distributed network with different aspects of event knowledge associated with a specific (or set of) neural structure(s). Based on our theoretical framework (Wood and Grafman, 2003), we had predicted that the experience dimension would be associated with medial PFC activity — we proposed that this region stores representations of frequently performed, highly predictable event sequences. The present findings showed right medial PFC activation associated with the experience dimension. This finding replicates previous studies showing medial PFC modulation by predictable event sequences (Koechlin et al., 2000, 2002). This finding is also consistent with a lesion study that found that patients with right medial frontal lesions were the worst performers on a feeling-of-knowing task, in which participants were asked to make predictions about their ability to remember previously encountered information (Schnyer et al., 2004).

The orbitofrontal cortex (OFC) was implicated in the remaining two dimensions, with left OFC activation for the engagement dimension, and right OFC activation for the social valence dimension. The engagement dimension concerns personal involvement, which was positively correlated with social relatedness, and a high score indicates a highly personal event requiring a lot of attention. A high score on the social valence dimension (which was negatively correlated with social relatedness and valence) indicates a negative, non-social event. Therefore, both dimensions have a social element and, as such, were expected to activate orbitofrontal cortex (Blair and Cipolotti, 2000; Moll et al., 2002a; Hornak et al., 2003). These findings are consistent with Davidson's ideas of hemispheric differences associated with approach–withdrawal behaviors (Davidson, 2001).

Social valence also modulated activation in the amygdala and premotor cortex bilaterally, and in the left anterior cingulate. The amygdala activation was predicted (Lane et al., 1997; Tabert et al., 2001) and together with the right orbitofrontal activation probably represents the negative emotional aspect of the events. Involvement of the anterior cingulate and premotor cortex has been reported previously for non-social event knowledge (Wood et al., 2003).

All of the identified MDS dimensions were associated with different patterns of PFC activation. Task difficulty cannot account for these results since (i) it should give rise to PFC regions being activated consistently; (ii) the findings are based on parametric analyses and, therefore, there was no subtraction between potentially harder and easier conditions; and (iii) subject response latencies did not correlate with the dimension values, suggesting that the trials themselves did not systematically vary in difficulty with the dimension values. Taken together, this strongly suggests that the pattern of fMRI activation was not based on task difficulty.

General Discussion

The dimensions that we have identified in the present study are continua and it is unlikely that complete knowledge about events is stored modularly in a discrete region of the PFC. Rather, we propose that event knowledge is stored across a distributed network of PFC regions including OFC, medial PFC, anterior cingulate and premotor cortex (see Fig. 2).

Figure 2.

Summary of the event knowledge structure identified in the present study (bold italic indicates the variables identified by Forgas).

Figure 2.

Summary of the event knowledge structure identified in the present study (bold italic indicates the variables identified by Forgas).

These regions store different aspects of event knowledge that would be bound together for a particular event; for example, an event that is frequently performed, predictable and socially engaging should be represented across a network involving medial PFC and left OFC. Encoding and retrieval of knowledge about this event should involve these same regions. We would also predict that the relative profile of activation across the neural network would shift dynamically as the various dimensions of the event take on more or less importance.

The events used in this study emphasized the motoric (‘go down the aisles’, ‘scrape the surfaces’) and social (‘invite guests’, ‘socialize and have fun’) aspects of event knowledge rather than visual, auditory, spatial, causal or temporal aspects. Few events emphasized the visual (‘look at the dinner menu’ is one), and none are explicitly auditory (‘turn off the alarm’ comes closest). Also, in accordance with Structured Event Complex (SEC) theory (Grafman, 2002), there is a strong planning component to many events (‘make a guest list’, ‘buy party invitation’), and a strong goal-directed element (‘shop for groceries’, ‘pay the cashier’). Our findings help characterize and pinpoint the distribution of event knowledge representation in the human PFC. The psychological structure of event knowledge for the types of events used in this study appears to be broadly organized along the dimensions of experience, engagement and social valence. Although the dimensions of event knowledge are both psychologically and neurally dissociable, event knowledge, in general, is represented in a network involving OFC and medial PFC, amygdala, anterior cingulate and premotor cortex.

It is likely that other manipulations of our experimental design or changes in subject strategy would have recruited additional areas in the PFC with additional distinctive representational assignments (see Wood and Grafman, 2003, for hypothesized examples). Nevertheless, this study demonstrates the importance of specific sectors of the PFC for mediating complex behaviors concerned with the categorization and understanding of routine social and non-social events. We propose that this knowledge is used on a daily basis by humans engaging in planning, social interactions and the assignment of relevance to our own and others' behaviors (Wood and Grafman, 2003; Wood et al., 2003). Our study affirms the importance and uniqueness of the human prefrontal cortex in representing knowledge about complex events.

Appendix: Activity Components

Non-social Activities


Activity
 

Activity component
 
Painting the house choose room 
 choose the paint type and color 
 buy paint 
 clean the surface ready to paint 
 scrape the surfaces 
 cover the furniture with sheets 
 stir the paint 
 paint the wall surfaces 
 let the first coat dry 
 apply second coat 
 clean the brushes 
 store the paint and equipment 
Getting ready for work turn off the alarm 
 wake up on time 
 get out of bed 
 brush your teeth 
 take a shower 
 shave with a razor 
 get breakfast ready 
 eat breakfast 
 read the newspaper 
 pack the lunch or snacks 
 gather your belongings and keys 
 go to work 
Doing the laundry gather the laundry 
 sort out clothes 
 go to the laundry room 
 put the clothes into the washer 
 set washer settings 
 turn the washer on 
 add the detergent 
 close the lid 
 empty the washer 
 put the clothes into the dryer 
 turn on the dryer 
 put the clothes away 
Shopping for groceries determine which items are needed 
 make a list 
 gather the coupons 
 get the cart 
 go down the aisles 
 shop for the groceries 
 go to checkout 
 put the groceries on the belt 
 pay the cashier 
 put the groceries in the car 
 unload the groceries from the car 
 put groceries away 

Activity
 

Activity component
 
Painting the house choose room 
 choose the paint type and color 
 buy paint 
 clean the surface ready to paint 
 scrape the surfaces 
 cover the furniture with sheets 
 stir the paint 
 paint the wall surfaces 
 let the first coat dry 
 apply second coat 
 clean the brushes 
 store the paint and equipment 
Getting ready for work turn off the alarm 
 wake up on time 
 get out of bed 
 brush your teeth 
 take a shower 
 shave with a razor 
 get breakfast ready 
 eat breakfast 
 read the newspaper 
 pack the lunch or snacks 
 gather your belongings and keys 
 go to work 
Doing the laundry gather the laundry 
 sort out clothes 
 go to the laundry room 
 put the clothes into the washer 
 set washer settings 
 turn the washer on 
 add the detergent 
 close the lid 
 empty the washer 
 put the clothes into the dryer 
 turn on the dryer 
 put the clothes away 
Shopping for groceries determine which items are needed 
 make a list 
 gather the coupons 
 get the cart 
 go down the aisles 
 shop for the groceries 
 go to checkout 
 put the groceries on the belt 
 pay the cashier 
 put the groceries in the car 
 unload the groceries from the car 
 put groceries away 

Social Activities


Activity
 

Activity component
 
Going out for dinner choose the restaurant 
 put your clothes on 
 drive to the restaurant 
 enter restaurant 
 wait to be seated 
 be seated at the table 
 look at the dinner menu 
 order the wine and any other drinks 
 order the meal 
 eat dinner 
 get the check 
 pay the check 
Putting a child to bed bathe the child 
 put the child into bed 
 pick out a story 
 read the story 
 say prayers 
 tuck the child in 
 turn the night light on 
 turn lights off 
 kiss the child 
 say goodnight to child 
 leave the child's bedroom 
 adjust the door 
Going bowling choose a bowling alley 
 call the bowling alley 
 choose a lane 
 go to the bowling alley 
 meet your friends at the alley 
 get score card 
 rent bowling shoes 
 find a ball 
 set up the score sheet 
 start bowling 
 return the shoes 
 pay for the game 
Giving a party make a guest list 
 invite guests 
 buy party invitations 
 determine the number of guests 
 buy the party food 
 buy the ice and alcohol 
 prepare the food and drink 
 put up decorations 
 plan the entertainment 
 greet the guests 
 serve the food 
 socialize and have fun 

Activity
 

Activity component
 
Going out for dinner choose the restaurant 
 put your clothes on 
 drive to the restaurant 
 enter restaurant 
 wait to be seated 
 be seated at the table 
 look at the dinner menu 
 order the wine and any other drinks 
 order the meal 
 eat dinner 
 get the check 
 pay the check 
Putting a child to bed bathe the child 
 put the child into bed 
 pick out a story 
 read the story 
 say prayers 
 tuck the child in 
 turn the night light on 
 turn lights off 
 kiss the child 
 say goodnight to child 
 leave the child's bedroom 
 adjust the door 
Going bowling choose a bowling alley 
 call the bowling alley 
 choose a lane 
 go to the bowling alley 
 meet your friends at the alley 
 get score card 
 rent bowling shoes 
 find a ball 
 set up the score sheet 
 start bowling 
 return the shoes 
 pay for the game 
Giving a party make a guest list 
 invite guests 
 buy party invitations 
 determine the number of guests 
 buy the party food 
 buy the ice and alcohol 
 prepare the food and drink 
 put up decorations 
 plan the entertainment 
 greet the guests 
 serve the food 
 socialize and have fun 

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