Event perception: a mind-brain perspective

Abstract

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.

Figure 1

Schematic depiction of the theory. Thin gray arrows indicate the flow of information between processing areas. Dashed lines indicate projections that lead to the resetting of event models. The connection from sensory inputs to event models is gated, such that event models receive sensory input only during the reset phase.

Figure 2

Graphical representation of the inputs and outputs to the Reynolds et al. (in press) neural network model. A–D show four consecutive frames of an actor chopping down a tree. The target output (dashed lines) on each frame is the model input (solid lines) on the subsequent frame. Frames C and D have similar inputs but dissimilar target outputs, illustrating the need to represent necessitating the representation of long-term sequential dependencies to achieve accurate prediction. Note. From “A computational model of event perception from perceptual prediction,” by J. R. Reynolds, J. M. Zacks, and T. S. Braver, in press, Cognitive Science. Copyright 2006 by the authors. Reprinted with permission.

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. Note. Adapted from “Using movement to understand simple events,” by J. M. Zacks, 2004, Cognitive Science, 28, p. 979–108. Copyright 2006 by the author. Adapted with permission.

Figure 4

Excerpt from a narrative describing a woman on a backpacking trip. Sequences such as this were embedded throughout the narratives. A critical object (in bold) is introduced, followed by a sentences that either contains a time shift (an hour later) or a control phrase (a moment later). Finally an anaphor (underlined) refers back to the critical object. (See Speer, et al., 2005.)

Figure 5

The left panel shows that readers were slower to process sentences if they contained a time shift (“an hour later”) than a control phrase (“ a moment later”). As is seen in the middle panel, they were also slower to read a following sentence that contained a reference to a previously mentioned object. Finally, the right panel shows that after reading a time shift sentence, critical objects were correctly recognized less often. (See Speer, et al., 2005.)

Figure 6

Schematic depiction of the model, with hypotheses about the neurophysiological structures corresponding to the different components of the model. Thin gray arrows indicate the flow of information between processing areas, which are proposed to be due to long-range excitatory projections. Dashed lines indicate projections that lead to the resetting of event models. Abbreviations: PFC = prefrontal cortex; IT = inferotemporal cortex; MT+ = human MT complex; pSTS = posterior superior temporal sulcus, ACC = anterior cingulate cortex, SN = substantia nigra, VTA = ventral tegmental area, LC = locus ceruleus. Gray arrows indicate the flow of information between processing areas.

Figure 7

Focal brain activity in three regions, showing transient changes at event segment boundaries (identified by vertical lines). Panels a–c depict the bilateral extrastriate and right frontal clusters of activated voxels. All regions showed reliable responses to event boundaries during passive viewing, and larger responses during active segmentation. The left image shows the extent of the cluster, superimposed on an averaged anatomical image for the 16 participants. The two graphs to the right show the evoked response during coarse and fine event units for passive viewing and active segmentation (see Zacks, et al., 2001).