People perceive and conceive of activity in terms of discrete events. Here the authors propose a theory according to which the perception of boundaries between events arises from ongoing perceptual processing and regulates attention and memory. Perceptual systems continuously make predictions about what will happen next. When transient errors in predictions arise, an event boundary is perceived. According to the theory, the perception of events depends on both sensory cues and knowledge structures that represent previously learned information about event parts and inferences about actors' goals and plans. Neurological and neurophysiological data suggest that representations of events may be implemented by structures in the lateral prefrontal cortex and that perceptual prediction error is calculated and evaluated by a processing pathway, including the anterior cingulate cortex and subcortical neuromodulatory systems. Keywords: events, attention, memory, prefrontal cortex, orienting response What Is an Event? The world as presented to human sense organs is continuous, dynamic, and fleeting. Yet people seem to perceive events as stable entities, to identify parts of events and their relations to other parts. For example, one might describe baking cookies by listing the following parts: "Preheating the oven, mixing the ingredients in a bowl, putting the dough on a cookie sheet . . ." This could reflect mere happenstance or accidents of linguistic structure, but a growing body of research suggests that talk of discrete events reflects a deeper psychological reality, that people perceive activity in terms of discrete events, that ongoing processing resources are devoted to this perceptual process, and that the online perception of events determines how episodes are encoded in memory ). Thus, events are key components of perception, attention, and memory. In this article, we present a theory of the perception of everyday events, review psychological data that have informed the theory, and discuss possible neural substrates of the theory's components. We begin with the formal definition of an event: "a segment of time at a given location that is conceived by an observer to have a beginning and an end" (Zacks & Tversky, 2001, p. 17). This definition is useful but surely does not exhaust the common conception of an event. The everyday notion of an event probably has a family-resemblance structure, with some highly typical members such as weddings and breakfasts and some atypical members such as the decay of a radioactive atom or the melting of a pond. Typical events seem to share some common features. They range from a few seconds (eating a strawberry) to a few hours (going for a hike). They are directed toward a goal; the goal of a wedding is to formalize a union, and the goal of breakfast is to sate one's hunger. Events involve animate agents, often human. These features, however, are neither necessary nor sufficient. Some events are quite short (cutting a ribbon) or quite long (World War II). Events that are natural occurrences, such as landslides, may lack both goals and animate agents. So, the taxonomic boundaries of the category event are fuzzy. The spatial and temporal boundaries of events also can be fuzzy-it is sometimes difficult to say where or when one event ends and another begins. Neither taxonomic fuzziness nor boundary fuzziness is a particular problem for the psychology of events, and both are exactly analogous to the psychology of objects. Here, we are concerned with the core of the category event: events that involve goal-directed human activity and are of modest duration (seconds to tens of minutes). (For comparative reviews of conceptions of events in psychology, see Stränger & Hommel, 1996; Shipley, in press.) In the next section, we present a theory of how human observers segment continuous activity into discrete events. In subsequent sections, we discuss evidence in support of this theory and its implications. Perception can be described as a roughly hierarchical process in which sensory information is successively transformed into representations that form the basis for action. Particularly important are representations of states of the world in the near future, which may be called perceptual predictions. Perceptual predictions are valuable because they allow an organism to anticipate the future and to 2007, Vol. 133, No. 2, 273-293 0033-2909/07/$12.00 DOI: 10.1037/0033-2909 273 plan appropriate actions rather than merely react to incoming stimuli. Such representations are critical for avoiding interception by predators, intercepting prey, and coordinating behavior with others. To the extent that information processing is hierarchical, perceptual predictions arise late in the processing hierarchy because incoming sensory information is transformed to generate predictions. In addition to being hierarchical, perception can be described as recurrent: Later processing stages affect the flow of processing in earlier stages. Finally, perception can be described as cyclical: Perceptual predictions are constantly compared with what actually happens, and these comparisons are used to guide ongoing processing. These three notions-hierarchy, recurrence, and cyclicality-are working assumptions in many different theories of perception (Neisser, 1967) , neurophysiology (Carpenter, & Grossberg, 2003; Fuster, 1991) , and language processing (van Dijk & Kintsch, 1983). They have been developed perhaps most fully in recurrent neural network models, which have been applied to word learning (Elman, 1990) , to action learning (Jordan & Rumelhart, 1992) , and to event perception (C. Hanson & Hanson, 1996) . The theory presented here, which we call event segmentation theory (EST), shares these three properties. In this section, we describe the theory in information-processing terms. Later in the article, we recast the theory in terms of the neural systems that may implement these information-processing components. EST proposes that event segmentation arises from the perceptual processing stream depicted in Figure 1 . Its core is a pathway whose input is a set of sensory representations and whose output is a set of perceptual predictions. The sensory inputs correspond to the information conveyed by the peripheral nervous system to the cortex. In the visual modality, for example, this corresponds to basic information about brightness, color, and possibly some preliminary edge extraction. Sensory inputs are transformed by perceptual processing to produce multimodal representations with rich semantic content, encoding information such as object identity and location, motion trajectories, and the identities and attitudes of other people. According to the theory, processing is oriented in time such that it results in predictions about the future state of perceptual representations. For example, extracting a motion contour leads to predictions about the future locations of objects, and inferring the goals of a person leads to predictions about his or her future movements. We propose that perceptual processing is guided by a set of representations called event models that bias processing in the perceptual stream. An event model is a representation of "what is happening now," which is robust to transient variability in the sensory input. The stability of event models over time is a source of perceptual constancy; an ongoing event is a single entity despite potential disruptions in sensory input such as occlusion or distraction. In this regard, event models are similar to the object files proposed to mediate object constancy in visual perception (Kahneman, Treisman, & Gibbs, 1992) or the short-term action representations proposed to mediate perceptual constancy in biological motion (Stränger & Hommel, 1996 , see section 5). However, event models are hypothesized to be active over much longer time frames than object files or biological motion representations. In terms of the current theory, object files and biological motion representations are hypothesized to be part of high-level perceptual processing components. Event models are working memory representations, which are implemented by transient changes in neural activation rather than long-term changes in synaptic weights. They are not necessarily accessible to consciousness, though people may have partial awareness of their contents under some circumstances. Event models are multimodal, integrating information from visual, auditory, and the other sensory modalities. In these regards they are akin to the representations recently proposed by Baddeley ( as forming an episodic buffer. Most of the time, the contents of event models are insensitive to immediate sensory and perceptual input, providing a stable representation of the current event to guide perceptual processing. This is indicated by the gated arrow terminating on the event models component in Figure 1 . Event models also receive input from event schemata-semantic memory representations that capture shared features of previously encountered events. Event schemata contain previously learned information about the sequential structure of activity. Unlike event models, event schemata are implemented by permanent synaptic changes. The information they store includes distinctive physical features such as object and actor movement, statistical information about which patterns of activity are likely to follow a given pattern, and information about actors' goals. The quality of perceptual prediction depends critically on whether one's current event models are a good fit to what is actually happening. Prediction quality is evaluated by an error detection mechanism that compares the perceptual processing stream's predictions with what actually happens in the world. Most of the time, the event models represent the current state of events well, and perceptual prediction is easy and accurate. From time to time, however, activity becomes less predictable, and the current contents of the event models become less useful for perceptual prediction. At these points, prediction error increases. EST proposes that at these points a gating mechanism detects these transient increases in prediction error and reacts to them by updating the event models. Updating consists of (a) resetting the current representations (indicated by the dashed black line in Figure 1 ) and (b) transiently increasing the influence of the pathway from sensory inputs to the event models. Together, these operations drive the event models into a new stable state. As the event models are updated, prediction error typically improves, and the influence of sensory inputs on the event models diminishes. The signal pathways into the event models can be thought of as controlled by a gate that swings open in response to increases in error prediction and then quickly swings shut again. Thus, the system alternates between long periods of stability and brief periods of change. Periods of stability are perceived by observers as events, and periods of change are perceived as the boundaries between events. The contents of event models are determined by a combination of bottom-up and top-down processing. When the event models' sensory inputs are transiently opened, they receive information from the current state of sensory and perceptual representations in a bottom-up fashion. Event schemata affect event models in a top-down fashion. (We propose that the influence of event schemata on event models is continuous and unaffected by the gating mechanism. However, this claim is based largely on parsimony and may need to be revised in the future.) At the same time that schemata influence the current contents of the event models, the event models' contents update the event schemata through a slow incremental learning process. Event models are active and accessible representations of the events that are currently underway, yet the amount of information contained in these models almost certainly exceeds what can be actively maintained in a limited capacity working memory system. The effective capacity of working memory can be augmented by the efficient use of previously stored knowledge representations. Ericsson and Kintsch (1995) proposed the construct of long-term working memory to describe these interactions. Information that is needed to perform skilled activities is rapidly encoded into longterm memory using knowledge structures that anticipate future retrieval demands. This information remains readily accessible as long as some part of it is available in short-term memory and can be rapidly reinstated once some part of the structure is retrieved. Developing effective long-term working memory for information in a particular domain is a skill that requires repeated practice with that domain. We presume that most human adults have had extensive experience with a wide range of events. Thus, event schemata help expand the effective capacity of event models by storing predictive information about the future relevance of certain aspects of events. Here is an example of how the mechanism in EST might work when one observes an everyday activity: Imagine watching a man wash dishes. He takes one plate from a pile next to the sink, scrapes food from it, and then places it in the sink. He does the same with a second plate. At this point, a number of cues make it likely that he will continue to scrape the plates. First, continuing to scrape would maintain a coherent movement pattern. Second, it would be consistent with previous observations in which it was statistically likely that scraping a plate was followed by more scraping of plates. Third, the observer might infer that the man had the goal of scraping all of the plates. Thus, for the duration of the plate-scraping activity, affairs would be predictable. However, when the man scraped the last plate, things would become less predictable. The coherent movement pattern would cease, the statistical dependency would be broken, and the inference of the actor's goal to scrape all of the plates would no longer have predictive value. At this point, perceptual prediction would decline, leading to the activation of the gating mechanism and updating of the event model. Reynolds, Zacks, and Braver, (in press) have developed a neural network simulation that implements the core features of EST: perceptual prediction, activation-based event models, and errorbased gating of those models. The network is presented with animations of a human actor performing simple actions (e.g., jumping jacks), represented as the three-dimensional location of 18 points on the actor's body (see Figure 2) . At each timepoint, the network attempts to predict the actor's body position at the next timepoint. The network is trained on a corpus of stored events, such that each event is presented from start to finish, but each event can be followed by any randomly chosen event from the corpus. The network has pools of units corresponding to sensory inputs, perceptual processing, predicted future inputs, and event models, as described in EST (see Figure 1 ). (Event schemata are not implemented in the simulation.) The pathway from sensory inputs to predicted future outputs is fully connected in a feedforward fashion. The event model units have connections from sensory inputs that are gated; they open only at transient increases in prediction error. Simulations using this network provide support for the basic architecture of EST: Throughout training, boundaries between individual events are associated with larger prediction errors than are within-event time points. Further, the network uses these transient spikes in prediction error to update stable representations differentiating among possible events. These event representations improve performance on the prediction task. With ap-275 EVENT PERCEPTION propriate gating, the event model representations can self-organize without any explicit labeling or categorization of the events. Thus, the proper gating and maintenance of event models aids perception. Note that the dish-scraping example and the Reynolds et al. (in press) network focus on one timescale in which activity becomes less predictable at the end of the dish-scraping or at the end of the individual events in the network's corpus. However, variations in predictability are to be expected on finer and coarser timescales as well. For example, it is likely that as each dish-scraping comes to an end, there is a small, brief increase in prediction error and that as the dishwashing activity comes to an end, there is a larger, longer increase in prediction error. We hypothesize that the architecture in EST is implemented simultaneously on a range of timescales, spanning from a few seconds to tens of minutes. For each timescale, the error signal is integrated to provide a reset signal tuned to the appropriate grain. Fine-grained representations are tuned such that they can be updated in response to small, brief increases in prediction error. For coarse-grained representations, the error signal is integrated with a longer time constant such that resets happen only in response to larger, more sustained increases in error. In addition to simultaneous parsing on multiple timescales, it is possible that event segmentation sometimes tracks simultaneous activities in parallel. For example, when attending a child's birthday party, one might simultaneously segment the actions of children playing a birthday game and those of parents having a conversation at the same time. It is an empirical question whether such parallel processing occurs or whether observers are limited to processing one activity stream at a time. According to EST, the segmentation of activity into events happens on an ongoing basis and plays two general and central roles in regulating perception and cognition. First, event segmentation controls the allocation of cognitive resources over time. During periods of low prediction error, the pathway from sensory inputs to event models is inactivated, and the event models are stable, which conserves resources. More intensive processing occurs transiently when prediction error increases, and the event models are reset. This regulation of resources over time can be viewed as a form of attention, focusing processing resources adaptively at those moments when incoming sensory information is most behaviorally relevant (Coull & Nobre, 1998; Nobre, 2001) . In other words, event segmentation does not itself require attention; rather, it implements a mechanism of attention. Second, event segmentation controls the updating of information in working memory by resetting the event models. The term cognitive control has been used to describe the control of attention and working memory in a variety of task domains (Cohen, Braver, & O'Reilly, 1996; Posner & Snyder, 1975) . In our view, EST's proposal that event segmentation controls resource allocation and updates memory is a claim that event segmentation is a core, domain-general mechanism of cognitive control. Our proposal that event models maintain stable representations that influence perceptual processing and are updated in response to errors of prediction is similar to proposals of several neural network models. In these networks, transient updating based on failures of prediction is a means of balancing stability and flexibility: Representations need to be stable across moment-tomoment fluctuations in perceptual input but need to be updated when they are no longer appropriate. These considerations have played a large role in the development of adaptive resonance theory (ART; Grossberg, 1999; Carpenter & Grossberg, 2003) . In ART networks, perceptual input is "cleaned up" by recurrent interactions with a stable high-level representation. This high-level representation forms an abstraction of the perceptual input over some period of time. When the cleanup process leads to large distortions of the perceptual input, the current high-level representation is deemed no longer appropriate, and a search is initiated for a new high-level representation. Our proposal differs from the one instantiated in ART networks most significantly in that error comparison is made between a predicted perceptual state and the actual perceptual input. In ART networks, there is no intrinsic orientation of the processing stream with respect to time.  Similar concerns motivate the architecture of several recent models of prefrontal cortex (PFC). In these models, PFC represents current goals for action and the means to achieve them. Goal representations must be stable until the goal is achieved (or blocked) in order to be effective; however, once a goal is no longer relevant, a new goal representation is desirable. In a broad theoretical review, Miller and Cohen (2001) suggested that updating of PFC representations could be gated by phasic dopamine signals from midbrain dopamine neurons (Braver & Cohen, 2000) , triggered by encountering unexpected rewards; other models address the possibility that unexpected lack of reward may also trigger the updating of memory representations (O'Reilly, Noelle, Braver, & Cohen, 2002; Rougier, Noelle, Braver, Cohen, & O'Reilly, 2005; Rougier & O'Reilly, 2002) . EST differs from this family of models in two regards. First, stable representations contain information about the state of the world rather than goals and the means to achieve them. Second, gating in this family model is based on failure to predict the reward value of a situation, whereas gating in EST is based on failure of perceptual prediction. Finally, a model of frontal cortex proposed by Frank and colleagues also includes stable representations that are occasionally updated through a gating mechanism (Frank, Loughry, & O'Reilly, 2001; Frank, Seeberger, & O'Reilly, 2004) . However, in this model, the gating mechanism is implemented by loops through frontal cortex, the basal ganglia, and thalamus, with subcortical structures modulating the excitability of frontal cortex and thereby determining whether it is stable or plastic. This approach, like Miller and Cohen's (2001) model and related proposals, focuses on representations required for action rather than for perception. Unlike dopamine accounts, this model provides a natural mechanism for selectively updating some representations without disrupting others. However, in the context of event perception, a more global signal might be more appropriate. In EST, we propose that the complete representation of an event at a given timescale is updated based on a relatively global signal, such as could be provided by the catecholamine neurotransmitters dopamine and norepinephrine. By varying the time constant over which this signal is integrated, selective updating of fine-grained or coarse-grained event models can be accomplished. EST thus differs from the model proposed by Frank and colleagues in three substantial ways: first, by basing the gating mechanism on the failure of perceptual prediction rather than on reward; second, by characterizing perception rather than action; and third, by adopting a less selective updating mechanism more compatible with implementation by neuromodulatory neurotransmitters than by corticothalamic loops. The principal novel features of EST are that event models maintain stable representations of "what is happening now" and are updated based on transient increases in perceptual prediction error. The theory has several implications for perception and cognition: 1. Most important, the theory implies that the segmentation of ongoing activity into discrete events is a spontaneous concomitant of ongoing perception and does not require conscious attention. 2. Event segmentation is a mechanism of cognitive control. The gating mechanism resets event models and thus is the means by which the cognitive system exerts control over the disposition of processing resources and the updating of working memory. 3. Event segmentation happens simultaneously on multiple timescales, though an observer may attend to a particular timescale. 4. Event segmentation incorporates information from multiple senses. This results from the fact that the mechanisms in EST are general across sensory modalities and incorporate information from multiple modalities. 5. Event segmentation depends on change. When the world is static, prediction is easy. 6. Event segmentation depends on prior knowledge. Event models are constructed through the interaction of sensory input with stored knowledge (including the knowledge stored in event schemata). In the following two sections, we review research on the cognitive and neural correlates of event segmentation with these implications in mind. The first section (Causes and Consequences of Event Segmentation) describes behavioral data that provide support for the model. The second section (Neural Correlates of Event Segmentation) describes proposals for how the nervous system may implement the information-processing model and a review of the relevant neuropsychological and neurophysiological data that motivate these proposals. Causes and Consequences of Event Segmentation The first question that comes up in asking how people perceive temporal structure in events is, How can one measure it? Newtson (1973) introduced a simple and surprisingly powerful solution to this problem, which he dubbed unitization (for reviews, see Newtson, 1976; Stränger & Hommel, 1996; . Participants are asked to watch movies of everyday activities (such as a person filling out a questionnaire) and to press a button whenever they judge that a boundary between successive events has occurred. We use the term unitization to refer to this task and to distinguish it from event segmentation, which we hypothesize is an ongoing perceptual process that is independent of any intentional task. When performing a unitization task, participants may be asked to identify the largest events they find meaningful (coarse-grained unitization) or the smallest (fine-grained unitization). With a little bit of practice, people generally have no problem following the instruction, and this simple task has produced rich and replicable phenomena. By two measures, reliability of unitization is good. First, participants show good agreement regarding the location of event boundaries (Newtson, 1976) . Second, the differences that do exist among observers can be attributed, in part, to stable individual differences rather than to noise: test-retest studies have found good reliability both in the length of the units people identify (Newtson, 1976) and in the particular locations they mark as event boundaries (Speer, Swallow, & Zacks, 2003) . The reliability of the unitization procedure provides support for EST's claim that event segmentation is a spontaneous concomitant of ongoing perception; the procedure corresponds intuitively to replicable aspects of observers' ongoing experience. The original interest in event unitization concerned social cognition: How does the grain at which people segment an activity affect observers' attributions about why actors perform particular actions? Newtson (1973) asked participants to segment movies of a man filling out a questionnaire or building a model molecule into either fine-grained or coarse-grained events and then to make judgments about the activity that had been performed. When observers were asked to segment activity into fine-grained events, they were more likely to draw conclusions about actors' permanent traits (e.g., personality characteristics). They also formed impressions of the actors' traits that were more differentiated and were more confident in their judgments. This association between finegrained units and dispositional attributions was supported correlationally in one subsequent study (Wilder, 1978b) but not in another (Wilder, 1978a) . The grain at which observers segment events also affects the dispositional attributions observers make. In one study, participants judged an actor as more likable if they had unitized her actions into fine-grained units rather than coarse-grained units (Lassiter, 1988) . If people form systematically different impressions when they attend to fine-grained than coarse-grained events, one reasonable possibility is that observers adaptively modulate the grain at which they segment activity in response to the needs of the situation. Newtson (1976) proposed that fine-grained unitization is more resource demanding than coarse-grained unitization and that observers unitize at the coarsest grain they can sustain while maintaining a coherent representation of the ongoing activity. Activity that is relatively coherent and predictable can be parsed at a coarse grain, whereas activity that is confusing or surprising must be parsed at a finer grain. This assumes that observers can perceive boundaries only at one grain at any time, in which case it is intuitive that identifying few units would be less resource demanding than identifying many units. Evidence for this proposal has accrued from studies in which a single surprising action was inserted into an otherwise predictable activity (Newtson, 1973) and from studies in which the overall predictability of the activity was manipulated more systematically (Wilder, 1978a (Wilder, , 1978b . EST provides an alternative account of these findings. The theory asserts that people do not perceive event boundaries on only one timescale. Rather, they perceive event boundaries on multiple timescales simultaneously but selectively attend to one timescale in response to instructions or other experimental manipulations. According to this view, when activity is coherent, participants segment it at multiple timescales and can choose to attend to finer or coarser grains. Attending to coarser grains may be preferred because it reduces the frequency of decision making and button pressing. However, when activity is less coherent, coarse-grained event segmentation may break down because prediction error will be uniformly high on coarse timescales, leaving only fine-grained segmentation intact. Data from experiments in which people segment the same activities multiple times at different timescales support this hypothesis (Hard, Lozano, & Tversky, 2006; Hard, Tversky, & Lang, in press; Lozano, Hard, & Tversky, in press; Speer et al., 2003; Zacks, Tversky, & Iyer, 2001 ). In these experiments, participants segmented activities at both a coarse and a fine grain, on different viewings. If viewers segmenting at a fine grain were spontaneously grouping fine-grained events into larger units, one would expect coarse-grained event boundaries to be a subset of fine-grained event boundaries. As a result, one would expect coarse and fine event boundaries to be aligned such that each coarse boundary would be placed close to a fine boundary. This pattern has been observed in several studies (Hard et al., in press; Speer et al., 2003; Zacks, Tversky, & Iyer, 2001) . One also would expect that coarse-grained events would tend to enclose a set of fine-grained events, such that coarse boundaries would follow rather than precede the fine boundary to which they were closest; this has also been observed (Hard et al., 2006; Lozano et al., in press). The presence of relations in segmentation that span timescales indicates that the coarse-grained segmentation occurred even when participants were attending to fine-grained segmentation. According to EST, working memory representations (the event models) are updated selectively at those points in time that correspond to perceptual event boundaries. This means that perceptual information at those times receives more extensive processing than perceptual information from other points in time. This extra processing should result in better long-term memory for this information. Several converging measures suggest that perceptual information from event boundaries is preferentially accessible in longterm memory. In one experiment, participants watched a movie and then saw still pictures from event boundaries, points in between boundaries, and from a similar movie they had not seen. They then reported which pictures came from the movie they had watched (Newtson & Engquist, 1976) . Accuracy was higher for pictures taken from event boundaries. Another study found that descriptions of event boundaries from memory were richer and more detailed than descriptions of nonboundaries (Schwan, Garsoffky, & Hesse, 2000) . Also, the overall amount of information that can be recalled is affected by the grain of segmentation: Multiple studies have reported that fine-grained unitization of movies leads to more detailed recall of the events depicted than does coarse-grained unitization (C. Hanson & Hirst, 1989; Lassiter, 1988; Lassiter, Stone, & Rogers, 1988) . However, it is currently a matter of debate whether fine-grained unitization also improves recognition memory for depictions of events (C. Hanson & Hirst, 1991; Lassiter & Slaw, 1991) . These effects are consistent with the view that observers can selectively attend to one timescale while segmenting simultaneously at several timescales. According to the theory, when the surface structure of an event depiction aligns with its underlying event structure, the gating mechanism should operate efficiently, resulting in enhanced longterm recall for the events. Conversely, surface cues that conflict with the event structure of an activity may lead to poorer memory. Three studies have used film editing techniques to test these hypotheses. In the first study (Schwan & Garsoffky, 2004) , participants viewed short movies of everyday events and then recalled them. The movies were presented either intact, with deletions that corresponded to intervals surrounding event boundaries, or with deletions of intervals in between event boundaries. Memory for the edited movies with preserved event boundaries was as good as 278 ZACKS, SPEER, SWALLOW, BRAVER, AND REYNOLDS memory for intact movies, but memory for edited movies with deleted intervals around event boundaries was poorer. The second study examined longer events and manipulated cues to event structure by marking the event boundaries rather than deleting actions (Boltz, 1992) . In this series of experiments, participants viewed a feature film with no commercial breaks or a film with commercial breaks. The breaks corresponded to or conflicted with event boundaries. Commercials at event boundaries improved later recall of the activity, whereas commercials at nonboundaries impaired memory. Commercial breaks at event boundaries also improved memory for the temporal order of events. One other study (Schwan et al., 2000) manipulated the placement of cuts between different camera positions in short movies. Cuts at event boundaries improved memory for those points in time but had little effect on overall memory performance. It is notable that the placement of cuts in the Schwan et al. (2000) study produced relatively weak effects compared with the placement of commercials in the Boltz study. One possibility is that simple cuts are not sufficiently salient to affect the operation of the gating mechanism-at least for participants used to viewing movies and television. Another possibility is that the effects of event segmentation on memory are more pronounced at longer timescales. Together, these results converge in supporting a role for event segmentation in long-term memory encoding. Patterns of individual differences suggest a strong relation between one's ability to segment activity during learning and one's ability to recall it later. In one study (Zacks, Speer, Vettel, & Jacoby, 2006) , older adults with and without dementia of the Alzheimer's type (DAT) segmented movies of everyday events and then performed a recognition memory test for visual details from the movies. An individual's ability to properly segment the movies was evaluated by comparing her or his segmentation with the segmentation of the group as a whole. Group-typical segmentation was found to be a unique predictor of later memory, even after the presence of dementia and overall cognitive level were controlled. This is consistent with EST's proposal that event boundaries receive richer processing and thus that identifying the proper boundaries results in more effective encoding for long-term memory. The theory's proposal that event boundaries receive differential processing during perception has implications for the ability to use perceptual experiences when one learns new skills. The ability to segment an activity into the right units should be valuable in learning how to perform the actions that occur during the activity. Several studies of procedural learning from events support this hypothesis. In one series of experiments, participants learned from a movie how to assemble a TV cart or a construction-block model (Hard et al., 2006) . During the learning phase, they segmented the movie into events; afterwards, they built the TV cart or the model. A number of features of the situation were varied: whether the participants segmented at a coarse grain, a fine grain, or both (and, if so, in what order); whether the participants described the activity while segmenting; and whether they were explicitly instructed to group fine-grained events into larger structures. Across experimental conditions, a consistent pattern was observed: Those individuals whose segmentation was more hierarchically structured were better at assembling the TV cart than those whose segmentation was less hierarchically structured. This was true across experimental conditions and across individuals within conditions. Participants' descriptions suggested that the benefit of hierarchical encoding was mediated by a tendency for those participants who segmented hierarchically to simulate the events from the actor's point of view. A subsequent series of experiments experimentally manipulated perspective taking and supported the hypothesis that hierarchical encoding facilitated simulating the actor's perspective (Lozano et al., in press). In a final procedural learning study (Zacks & Tversky, 2003) , participants learned about an everyday procedure, such as putting together a musical instrument using a computer program. The program was designed to visually depict a set of presupposed coarse-grained event boundaries in the procedure or to conflict with those boundaries. In one experiment, event boundaries were identified by asking a separate group of participants to segment a movie of the activity to be taught. In this experiment, visual structure that coincided with these boundaries improved memory for the order of events. In another experiment, event boundaries were taken from manufacturer's instructions, which may have conflicted with perceptually natural event boundaries. In this experiment, reinforcing the event boundaries reduced memory for the order of events. These results suggest that memory is facilitated when surface cues support the intrinsic event structure of an activity, and memory is impaired when surface structure conflicts with the intrinsic event structure. Together, studies of long-term memory for events and studies of learning procedures from events suggest that event boundaries are used to structure memory encoding. The fact that event boundaries are remembered better than nonboundaries supports EST's claim that event segmentation is a cognitive control mechanism. Further support for this claim is that individuals who are good at segmenting events remember them better than do individuals who are poor at segmenting. Moreover, cuing the appropriate event structure facilitates memory, whereas miscuing event structure impairs memory. Finally, people consistently segment activity hierarchically, making connections that span timescales. This supports the claim that event segmentation proceeds simultaneously on multiple timescales. According to EST, event segmentation depends on changes in the environment and on prior knowledge. Physical changes in the environment can drive event segmentation in a bottom-up fashion by increasing the potential for prediction error. When sensory inputs are constant, a well-tuned perceptual prediction system will adhere to the old adage about weather forecasting-"tomorrow will be the same as today"-and will be correct in this prediction. When changes occur there is more opportunity for error. At the same time, prior knowledge can influence event segmentation in a top-down fashion by biasing the system's interpretation of what a change portends. One type of prior knowledge can be tied to domain-specific reasoning about goals and plans. When watching goal-directed human activity, observers may infer an actor's goal on the basis of past experience or explicit instructions and use this information to bias perceptual prediction. For example, baseball fans can anticipate events that might come as a surprise to baseball novices (e.g., all the players running off the field at the end of an inning), and this is likely to influence event segmentation. Another 279 EVENT PERCEPTION type of prior knowledge involves domain-general statistical learning. Humans have powerful mechanisms for learning sequential dependencies in streams of information, which can influence motor performance (Seger, 1994) and language learning (e.g., Brent, 1999; Saffran, Aslin, & Newport, 1996) . Similar mechanisms may guide perceptual prediction. According to the model, prior knowledge about goals and plans and prior statistical learning are encoded in event schemata. A small body of evidence indicates that both physical changes and prior knowledge are correlated with the perception of event boundaries. One distinctive physical change that correlates with the perception of event boundaries is movement. In one study, participants unitized brief movies, and the experimenters identified those points that most observers agreed were event boundaries (Newtson, Engquist, & Bois, 1977) . The movies then were coded using a dance notation that provided a discrete representation of which actor joints had changed position by more than 45°during each 1-s interval. The number of joints that changed between successive intervals is a rough index of the amount of motion at that point in the movie. The number of changing joints was averaged at each grain for transitions into and out of event boundaries and for successive intervals within an event. Transitions into and out of event boundaries had statistically greater numbers of changes (i.e., more movement) than did successive within-event intervals. Converging evidence for a relation between movement features and segmentation comes from a recent study in which participants segmented animations of geometric objects playing simple games (Hard et al., in press). The animations were coded by eye for movement change such as stops, direction changes, and changes in speed. These movement changes were associated with increases in both fine-grained and coarse-grained segmentation. A pair of studies (summarized in Baird & Baldwin, 2001 ) suggests that event segmentation is correlated with actors' goals and plans. In one study, one group of participants was asked to segment movies of everyday activities based on when the actor completed a goal. A second group of participants watched sequences taken from these movies with brief tones placed either at points when goals were completed or at midpoints between goal completions. After each sequence, they were asked to watch it again and press a button to mark where the tone had occurred. Tones were remembered more accurately when they were placed at goal completions, whereas midpoint tones were remembered as having occurred closer to completions than they actually did. These effects on memory suggest that participants perceived the activity in terms of intention-based units, and this affected how the locations of tones were stored during the initial encoding phase. However, an alternative explanation is that knowledge structures representing goals biased performance only during the retrieval phase of the task. More direct evidence that goal completions are perceived as event boundaries comes from a second study, conducted with 10 -11-month-old infants. In this experiment, the infants were repeatedly exposed to a sequence taken from a movie of an everyday activity. They then were tested with one of two altered versions of the sequence. In one version, a pause was inserted at a moment that had been identified as the completion of a goal; in the other, a pause was inserted in the middle of two completions. The experimenters hypothesized that if the infants encoded the activity in terms of the actor's intentions, a pause in the middle of accomplishing a goal would be more surprising than a pause at the end of completing a goal. Thus, they should look longer at the version with a pause in the middle. This was exactly what the experimenters found: Infants looked longer at the altered sequences when those sequences contained a pause that interrupted an ongoing goal compared with when the pause occurred at a goal completion. A recent study provided evidence that both physical changes and goals are systematically related to event segmentation (Zacks, 2004) . In three experiments, participants viewed animations that showed the movements of a pair of objects (see Figure 3A) and unitized them to mark coarse-grained or fine-grained units. Two types of animation were shown: Goal-directed activity stimuli were generated by recording the actions of two people controlling Figure 3 . A: Trace of two randomly moving objects. From the time-varying x and y locations of the object, an exhaustive set of movement features was calculated, which included the objects' position, speed, and acceleration, the distance between the objects, and their relative speed and relative acceleration. B: Movement features were quite predictive of where observers segmented the activity when the activity was randomly generated and was unitized at a fine grain. The dependence of unitization on movement features was reduced for goal-directed human activity and for coarse-grained unitization.  ZACKS, SPEER, SWALLOW, BRAVER, AND REYNOLDS the objects in a simple video game, and random activity stimuli were generated by a random process designed to match the velocity and acceleration of the video game stimuli. Rather than hand coding movement features of interest to the experimenters (Hard et al., in press; Newtson et al., 1977) , a quantitative movement analysis was performed. For each animation, an exhaustive set of movement features was calculated, including the position, velocity, and acceleration of each object, the distance between the objects, their relative velocity, and their relative acceleration. For each experimental condition, the relation between the movement features and the probability that a participant would identify an event boundary was characterized using linear models. Movement features were significantly related to event unitization for all conditions, providing evidence that distinctive physical features play a role in event segmentation. Two further results suggested that the processing of movement information interacted with goal processing to determine event segmentation. First, the relation between movement features and unitization was consistently stronger for fine-grained unitization than coarse-grained unitization (see Figure 3B ). This suggests that movement features may play a particularly strong role in identifying the smallest units of activity but that other features may be important in identifying which of those low-level event boundaries are also boundaries between larger units of activity. Second, the relation between movement features and unitization was weaker for goal-directed activity than for random activity. This suggests that prior knowledge may play a role in modulating how physical characteristics are processed in order to identify event boundaries. Knowledge about actors' goals is one source of prior knowledge that can affect event segmentation. Simple statistical information is another complementary source of prior knowledge, which is also related to the perceptual segmentation of events. One series of studies indicated that similar mechanisms may play a role in the identification of events and their boundaries (Avrahami & Kareev, 1994) . In three experiments, participants viewed movies made by concatenating short clips from cartoon films. In the first experiment, the experimenters constructed a sequence of clips that included a perceptually salient change. These changes were identified by untrained participants as a part boundary. A second group of participants viewed a stream of clips that contained repeated presentations of the sequence. They were then shown the sequence in a short stream of clips and were asked where they thought it should be divided. These participants placed the boundary not at the salient change but at the end of the sequence. A second experiment showed that participants were more likely to recognize a sequence that repeated throughout a movie if the clips appearing before and after the sequence were varied. A final experiment showed a similar effect using a recall paradigm. These data suggest that the repetition of an arbitrary sequence across different contexts is sufficient for it to be conceived of as a coherent unit. However, it is important to note that in all three experiments the tasks depended heavily on memory, so the extent to which these data speak to perception, as such, is not clear. To summarize, studies of the unitization of events provide evidence consistent with EST's claims that event segmentation depends on change and that event segmentation depends on prior knowledge. The fact that event boundaries are correlated with changes in movement features in two quite different paradigms indicates that event segmentation depends on change. The fact that event boundaries are correlated with goals and with sequential statistical structure indicates that event segmentation depends on prior experience.
In further work Zacks et al. REF found evidence that when human perception does not match the internal prediction, an event boundary is perceived.
Event perception: A mind/brain perspective
Psychological Bulletin
Psychology Medicine