No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Attention reorganizes as structure is detected in dynamic action
Abstract
Once one sees a pattern, it is challenging to “unsee” it; discovering structure alters processing. Precisely what changes as this happens is unclear, however. We probed this question by tracking changes in attention as viewers discovered statistical patterns within unfolding event sequences. We measured viewers’ “dwell times” (e.g., Hard, Recchia, & Tversky, 2011) as they advanced at their own pace through a series of still-frame images depicting a sequence of event segments (“actions”) that were discoverable only via sensitivity to statistical regularities among the component motion elements. “Knowledgeable” adults, who had had the opportunity to learn these statistical regularities prior to the slideshow viewing, displayed dwell-time patterns indicative of sensitivity to the statistically defined higher-level segmental structure; “naïve” adults, who lacked the opportunity for prior viewing, did not. These findings clarify that attention reorganizes in conjunction with statistically guided discovery of segmental structure within continuous human activity sequences. As patterns emerge in the mind, attention redistributes selectively to target boundary regions, perhaps because they represent highly informative junctures of “predictable unpredictability.”
... A plausible explanation for the advantage of event boundaries is offered by EST: once an event comes to an end, a range of possible new events may follow; the transition from the end of one event to the beginning of the next is less predictable and thus requires more processing resources . In support of this idea, people spend more time at event boundaries when reading event descriptions or watching slideshows of events at their own pace Hard et al., 2019;Pettijohn & Radvansky, 2016). In this line of reasoning, attention is organized in line with event segmental structure, with more attention being allocated to the less predictable event boundaries. ...
... Such an outcome would be theoretically important: if boundedness is computed spontaneously during event understanding, it could offer a powerful way of organizing incoming event information, readily connect to the way events are encoded in language, and potentially have further effects on event cognition. For instance, recall that, according to a large body of work in cognitive psychology, event boundariesand especially, event endpoints -are privileged in both event comprehension and memory compared to other temporal slices of events Hard et al., 2019;Huff et al., 2012;Newtson & Engquist, 1976;Pettijohn & Radvansky, 2016;Schwan & Garsoffky, 2004;Swallow et al., 2009;. However, past studies on event 1 Spontaneous cognitive processes are unconscious and involuntary, even though their operation is determined by attention or some other form of calibration (Carruthers, 2017;O'Grady et al., 2020). ...
... Results from Experiment 1 show that viewers tended to miss visual breaks at the endpoint compared to the midpoint of dynamically unfolding events, in accordance with previous work pointing to the importance of event endpoints for event comprehension (Newtson & Engquist, 1976;Schwan & Garsoffky, 2004;Swallow et al., 2009;Hard et al., 2011;Hard et al., 2019;Pettijohn & Radvansky, 2016;Huff et al., 2012). Importantly, however, this effect was more potent for bounded (structured, non-homogeneous) compared to unbounded (non-structured, homogeneous) events. ...
A long philosophical and linguistic literature on events going back to Aristotle distinguishes between events that are internally structured in terms of distinct temporal stages leading to culmination (bounded events; e.g., a girl folded up a handkerchief) and events that are internally unstructured and lack an inherent endpoint (unbounded events; e.g., a girl waved a handkerchief). Here we show that event cognition spontaneously computes this foundational dimension of the temporal texture of events. People watched videos of either bounded or unbounded events that included a visual interruption lasting either 0.13 s (Experiment 1) or 0.03 s (Experiments 2 and 3). The interruption was placed at either the midpoint or close to the endpoint of the event stimulus. People had to indicate whether they saw an interruption after watching each video (Experiments 1 and 2) or respond as soon as they detected an interruption while watching each video (Experiment 3). When people responded after the video, they were more likely to ignore interruptions placed close to event endpoints compared to event midpoints (Experiment 1); similarly, when they responded during the video, they reacted more slowly to endpoint compared to midpoint interruptions (Experiment 3). Crucially, across the three experiments, there was an interaction between event type and interruption timing: the endpoint-midpoint difference depended on whether participants were watching an event that was bounded or unbounded. These results suggest that, as people perceive dynamic events, they spontaneously track boundedness, or the temporal texture of events. This finding has implications for current models of event cognition and the language-cognition interface.
... Though most previous work has focused on retrospective boundary identification, anticipatory processing has some preliminary support. When self-pacing through sequential images of action sequences, participants tend to dwell (or pause) on perceived boundary images (Hard et al., 2011(Hard et al., , 2019Kosie & Baldwin, 2019a, 2019b. Kosie and Baldwin (2019b) proposed that this dwell-time effect resulted from selective attention to moments of uncertainty afforded by perceiving a goalcompletion event. ...
... Tones associated with phrase beginnings and endings were unambiguously identified from notations in the musical score. This practice seems at least as objective as reliance on trained expert coders to determine event boundaries in research using visual action sequences (e.g., Hard et al., 2019;Kosie & Baldwin, 2019a, 2019b. We included both phrase endings and phrase beginnings as target tones to provide a strong test of entropy's role in segmentation, controlling for compositional cues in the melodies that might signal melodic phrase endings in other ways. ...
... This framework may also explain previously demonstrated dwell-time effects (Hard et al., 2011(Hard et al., , 2019Kosie & Baldwin, 2019a, 2019bKragness & Trainor, 2016, because there is a time delay associated with segmentation and reintegration into previous knowledge. This reintegration process, however, may have a cost. ...
Anticipating the future is essential for efficient perception and action planning. Yet the role of anticipation in event segmentation is understudied because empirical research has focused on retrospective cues such as surprise. We address this concern in the context of perception of musical-phrase boundaries. A computational model of cognitive sequence processing was used to control the information-dynamic properties of tone sequences. In an implicit, self-paced listening task ( N = 38), undergraduates dwelled longer on tones generating high entropy (i.e., high uncertainty) than on those generating low entropy (i.e., low uncertainty). Similarly, sequences that ended on tones generating high entropy were rated as sounding more complete ( N = 31 undergraduates). These entropy effects were independent of both the surprise (i.e., information content) and phrase position of target tones in the original musical stimuli. Our results indicate that events generating high entropy prospectively contribute to segmentation processes in auditory sequence perception, independently of the properties of the subsequent event.
... Another important aspect of forming loosely hierarchical structured event compressions lies in the prediction of event boundaries. Indeed, it was shown that having background knowledge about the ongoing activities while an event unfolds can help to predict when the current event might end [9]. This means that the developing event-generative compression structure may develop deeper knowledge about the currently unfolding event. ...
... In future work, we will integrate surprise estimates from the LSTMf module directly, as previous analyzed elsewhere [5]. Moreover, we intend to enhance the architecture further to enable it to predict event boundaries, whose detection initially correlates with measures of surprise [9] Finally, the architecture will be combined with the REPRISE mechanism and scaled to larger problem domains, including robotic control and object manipulation tasks. ...
Our brain receives a dynamically changing stream of sensorimotor data. Yet, we perceive a rather organized world, which we segment into and perceive as events. Computational theories of cognitive science on event-predictive cognition suggest that our brain forms generative, event-predictive models by segmenting sensorimotor data into suitable chunks of contextual experiences. Here, we introduce a hierarchical, surprise-gated recurrent neural network architecture, which models this process and develops compact compressions of distinct event-like contexts. The architecture contains a contextual LSTM layer, which develops generative compressions of ongoing and subsequent contexts. These compressions are passed into a GRU-like layer, which uses surprise signals to update its recurrent latent state. The latent state is passed forward into another LSTM layer, which processes actual dynamic sensory flow in the light of the provided latent, contextual compression signals. Our model shows to develop distinct event compressions and achieves the best performance on multiple event processing tasks. The architecture may be very useful for the further development of resource-efficient learning, hierarchical model-based reinforcement learning, as well as the development of artificial event-predictive cognition and intelligence.
... Similarly, in event segmentation, recognizing the goal of the individuals involved in the scene allows the viewer to recognize the structure inherent in the actions as they unfold (e.g. Hard, Meyer, & Baldwin, 2019;Magliano, Radvansky, Forsythe, & Copeland, 2014). The relational structure associated with the goal organizes information and specifies the role of a given item or attribute in terms of how it relates to the situation. ...
Within a given situation, an individual's goal motivates and structures how they interact with their surroundings. The goal also organizes the available information and specifies the role of a given item or attribute in terms of how it relates to the other aspects of the situation. We propose these ideas should inform the study of concept acquisition. There is abundant evidence that the goal orients an individual to goal-relevant attributes of items during concept acquisition. A more speculative claim is that the goal structures the conceptual knowledge acquired. We introduce a new paradigm for examining goal-directed concept acquisition (Experiment 1) and then assess how both attention to an attribute and its goal-relevance affect its centrality within the acquired concept (Experiment 2). Participants were given items to use as they completed a specified task. In both experiments, we found evidence that task goals oriented participants to goal-relevant attributes of the items. Category-based ratings for items during a transfer task, as well as how the participants sorted the items into groups, indicated that the goal-relevant attributes were more central within the acquired concepts. In Experiment 2, we found that the goal-relevance of the attribute, beyond attentional allocation to the attribute during the task, affected the organization of attribute information within the acquired concept. These results support the thesis that information captured within the conceptual knowledge is structured with respect to the goal.
... The current results have important implications for theories of event comprehension, including event segmentation theory, which posits that event models are updated at spikes in prediction error (14,19,42,47,48). They suggest that other metrics of prediction quality-specifically prediction uncertainty-should be considered as potential gating mechanisms for human event segmentation (11,13,49,50). Benchmarking these two mechanisms against human performance required a large, naturalistic corpus of human activity recordings. ...
Humans form sequences of event models—representations of the current situation—to predict how activity will unfold. Multiple mechanisms have been proposed for how the cognitive system determines when to segment the stream of behavior and switch from one active event model to another. Here, we constructed a computational model that learns knowledge about event classes (event schemas), by combining recurrent neural networks for short-term dynamics with Bayesian inference over event classes for event-to-event transitions. This architecture represents event schemas and uses them to construct a series of event models. This architecture was trained on one pass through 18 hours of naturalistic human activities. Another 3.5 hours of activities were used to test each variant for agreement with human segmentation and categorization. The architecture was able to learn to predict human activity, and it developed segmentation and categorization approaching human-like performance. We then compared two variants of this architecture designed to better emulate human event segmentation: one transitioned when the active event model produced high uncertainty in its prediction; the other transitioned when the active event model produced a large prediction error. The two variants learned to segment and categorize events, and the prediction uncertainty variant provided a somewhat closer match to human segmentation and categorization—despite being given no feedback about segmentation or categorization. These results suggest that event model transitioning based on prediction uncertainty or prediction error can reproduce two important features of human event comprehension.
... Although it has been proven that direct attention is not crucial to the process of implicit learning, attentional focus positively impacts the behavioral changes of statistical learning (Musz, Weber, & Thompson-Schill, 2015;Turk-Browne et al., 2005). Furthermore, attention can be reallocated through predictive computations (Hard, Meyer, & Baldwin, 2019;Alamia & Zénon, 2016). Besides the close relationship between attention and statistical learning, alpha oscillation and both spatial and temporal attention have been linked as well (Klimesch, 2012;Foxe & Snyder, 2011;Hanslmayr, Gross, Klimesch, & Shapiro, 2011). ...
The predictability of a stimulus can be characterized by its transitional probability. Perceptual expectations derived from the transitional probability of the stimulus were found to modulate the early alpha oscillations in the sensory regions of the brain when neural responses to expected versus unexpected stimuli were compared. The objective of our study was to find out the extent to which this low-frequency oscillation reflects stimulus predictability. We aimed to detect the alpha-power difference with smaller differences in transitional probabilities by comparing expected stimuli with neutral ones. We studied the effect of expectation on perception by applying an unsupervised visual statistical learning paradigm with expected and neutral stimuli embedded in an image sequence while recording EEG. Time–frequency analysis showed that expected stimuli elicit lower alpha power in the window of 8–12 Hz and 0–400 msec after stimulus presentation, appearing in the centroparietal region. Comparing previous findings of expectancy-based alpha-band modulation with our results suggests that early alpha oscillation shows an inverse relationship with stimulus predictability. Although current data are insufficient to determine the origin of the alpha power reduction, this could be a potential sign of expectation suppression in cortical oscillatory activity.
... For instance, emerging from the subway and stepping into a city park can trigger an update in the ongoing event model that generates new predictions for perceptual inputs that one may encounter in the context of the park. A subset of the external change category reflects event segmentation from an observer's perspective, commonly used in laboratory experiments of event segmentation, such as when a person is watching a film or reading a narrative where there is a change in the actor's activity or a phrase indicating time passing (Ezzyat & Davachi, 2011;Hard et al., 2019;Schwan et al., 2000;Speer & Zacks, 2005). In this case, the environment and changes thereof are largely constructed based on the individual's internal representations and event schemas. ...
Our daily experiences unfold continuously, but we remember them as a series of discrete events through a process called event segmentation. Prominent theories of event segmentation suggest that event boundaries in memory are triggered by significant shifts in the external environment, such as a change in one’s physical surroundings. In this review, we argue for a fundamental extension of this research field to also encompass internal state changes as playing a key role in structuring event memory. Accordingly, we propose an expanded taxonomy of event boundary-triggering processes, and review behavioral and neuroscience research on internal state changes in three core domains: affective states, goal states, and motivational states. Finally, we evaluate how well current theoretical frameworks can accommodate the unique and interactive contributions of internal states to event memory. We conclude that a theoretical perspective on event memory that integrates both external environment and internal state changes allows for a more complete understanding of how the brain structures experiences, with important implications for future research in cognitive and clinical neuroscience.
... Memorability may instead reflect how information is organized and clustered in memory based on second-order statistics (e.g., similarity, entropy) across perceptual and semantic properties (Bainbridge, 2017;Bainbridge & Rissman, 2018). However. the current study focused on only two dance genres and a U.S. sample, so future work can expand to other dance styles (e.g., contemporary dance), movement types (e.g., more "everyday" motions), and audiences (beyond the United States), and consider other potential correlates to memorability (e.g., the flow vectors of the individual joints, motion energy [Nishimoto et al., 2011], or event structure [Hard et al., 2019;Zacks et al., 2001]). It is worth noting along these lines that ballet dance segments consistently showed lower consistency than the K-pop segments, which suggests that there may be important differences across genres worth exploring in future work. ...
Our most moving experiences, the ones that "stick," are hardly ever static but are dynamic, like a conversation, a gesture, or a dance. Previous work has shown robust memory for simple actions (e.g., jumping or turning), but it remains an open question how we remember more dynamic sequences of complex and expressive actions. Separately, with static images, previous work has found remarkable consistency in which images are remembered or forgotten across people-that is, an intrinsic "memorability"-but it is unclear whether semantically ambiguous and expressive actions might similarly be consistently remembered, despite the varying interpretations of what they could mean. How do we go from static memories to more memorable dynamic experiences? Using the test case of a rich and abstract series of actions from dance, we discover memorability as an intrinsic attribute of movement. Across genres, some movements were consistently remembered, regardless of the perceiver, and even regardless of the dancer. Among a comprehensive set of memory, movement, and aesthetic attributes, consistency in which movements people remembered was most predicted by subjective memorability, and importantly by both subjective (observer ratings) and objective (optical flow analysis) measures of the scale of motion, such that the less overall motion in a dance segment, the more memorable the movements tended to be. Importantly, we discover that memorability of a sequence is additive, where the memorability of individual snapshots and constituent moments ultimately contribute to the memorability of longer sequences. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
... The current results have important implications for theories of event comprehension, including event segmentation theory, which posits that event models are updated at spikes in prediction error (14,19,42,47,48). They suggest that other metrics of prediction quality-specifically prediction uncertainty-should be considered as potential gating mechanisms for human event segmentation (11,13,49,50). Benchmarking these two mechanisms against human performance required a large, naturalistic corpus of human activity recordings. ...
Humans form sequences of event models—representations of the immediate situation—topredict how activity will unfold. Two challenging questions about event models are “How isactivity segmented into events?” and “How is knowledge about different event types learned andrepresented?” We constructed a computational model combining a recurrent neural network forshort-term dynamics with Bayesian inference over event types for event-to-event transitions.This architecture learns schemas representing knowledge about event types and uses them,along with observed perceptual information, to construct a series of event models, updatingthem when predictions are violated. The architecture learned to predict activity dynamics fromone pass through an 18-hour corpus of naturalistic human activity. When tested on another 3.5hours of activities, it switched schemas at times corresponding with human segmentation andformed human-like event categories—despite being given no feedback about segmentation orcategorization. These results establish that event schemas and updating based on predictionerror can ground learning and can naturally reproduce two important features of humancomprehension.
... Across all three activity sequences, preschoolers' dwell times increased at the same goal-related event boundaries. These resultsusing three new activity sequences-replicate previous research, and confirm that preschoolers, like adults, proactively attend to goal-related segmental structure as they process unfolding activity (Hard et al., 2011(Hard et al., , 2019Kosie & Baldwin, 2019a, 2019bMeyer et al., 2011;Ross & Baldwin, 2015). ...
Using Hard et al.’s (2011) dwell‐time paradigm, 85 preschoolers (aged 2.5–4.5; 43 female; primarily from white families) advanced at their own pace through one of three slideshows. All slideshows depicted an actor reaching toward, grasping, and retrieving a ball. However, motion patterns differed for one slideshow (straight‐reach) relative to the other two (arcing‐reaches), and one of the arcing‐reach slideshows depicted a violation of typical goal‐related motion. Preschoolers’ knowledge of goal structure systematically modulated attention to event boundaries across slideshows despite surface differences, even when controlling for pixel change (an index of changes in motion). These findings showcase the value of the dwell time paradigm, and illuminate how children deploy attention as goal‐related expectations shape their analysis of continuously unfolding activity.
... On the other hand, it is likely that these two variables are measuring slightly different aspects of statistical learning. The ERPs are time-locked to the onset of the predictor stimuli before the target appears and therefore seem to reflect the recognition-i.e., a modulation of attention [11,71]-that certain predictors are cues for the occurrence of the target (i.e., a form of predictive processing). Alternatively, the reaction times are a measure of the behavioral responses following the occurrence of a target and therefore reflect the reaction to the target, and not a prediction that the target will occur. ...
Language is acquired in part through statistical learning abilities that encode environmental regularities. Language development is also heavily influenced by social environmental factors such as socioeconomic status. However, it is unknown to what extent statistical learning interacts with SES to affect language outcomes. We measured event-related potentials in 26 children aged 8–12 while they performed a visual statistical learning task. Regression analyses indicated that children’s learning performance moderated the relationship between socioeconomic status and both syntactic and vocabulary language comprehension scores. For children demonstrating high learning, socioeconomic status had a weaker effect on language compared to children showing low learning. These results suggest that high statistical learning ability can provide a buffer against the disadvantages associated with being raised in a lower socioeconomic status household.
... Explicit instructions turn automatic processing into intentional learning, which requires attention to what is to be learnt. In statistical learning, attention itself is modulated as learning progresses (35,36). Attention influences learning by facilitating encoding of particular aspects of the input (and explicit instructions provide participants with information about what aspects of the input they need to pay attention to -recurrence of sequences within a continuous stream). ...
The cognitive mechanisms underlying statistical learning are engaged for the purposes of speech processing and language acquisition. However, these mechanisms are shared by a wide variety of species that do not possess the language faculty. Moreover, statistical learning operates across domains, including nonlinguistic material. Ancient mechanisms for segmenting continuous sensory input into discrete constituents have evolved for general‐purpose segmentation of the environment and been readopted for processing linguistic input. Linguistic input provides a rich set of cues for the boundaries between sequential constituents. Such input engages a wider variety of more specialized mechanisms operating on these language‐specific cues, thus potentially reducing the role of conditional statistics in tokenizing a continuous linguistic stream. We provide an explicit within‐subject comparison of the utility of statistical learning in language versus nonlanguage domains across the visual and auditory modalities. The results showed that in the auditory modality statistical learning is more efficient with speech‐like input, while in the visual modality efficiency is higher with nonlanguage input. We suggest that the speech faculty has been important for individual fitness for an extended period, leading to the adaptation of statistical learning mechanisms for speech processing. This is not the case in the visual modality, in which linguistic material presents a less ecological type of sensory input.
... Statistical learning is a process for extracting statistical regularities from the environment that enables efficient processing of continuous sensory inputs. One of the tasks that relies on statistical learning is segmenting continuous inputs into discrete constituents (Baldwin, Andersson, Saffran, & Meyer, 2008;Gómez, Bion, & Mehler, 2011;Hard, Meyer, & Baldwin, 2019;Siegelman, 2019;Siegelman, Bogaerts, Armstrong, & Frost, 2019). It is generally assumed that statistical learning is incidental and happens without awareness and across modalities (Arciuli, von Koss Torkildsen, Stevens & Simpson, 2014;Aslin & Newport, 2012;Dienes, Broadbent, & Berry, 1991). ...
Despite theoretical debate on the extent to which statistical learning is incidental or modulated by explicit instructions and conscious awareness of the content of statistical learning, no study has ever looked into the metacognition of statistical learning. We used an artificial language learning paradigm and a segmentation task that required splitting a continuous stream of syllables into discrete recurrent constituents. During this task, statistical learning potentially produces knowledge of discrete constituents as well as about statistical regularities that are embodied in familiarization input. We measured metacognitive sensitivity and efficiency (using hierarchical Bayesian modelling to estimate metacognitive sensitivity and efficiency) to probe the role of conscious awareness in recognition of constituents extracted from the familiarization input and recognition of novel constituents embodying the same statistical regularities as these extracted constituents. Novel constituents are conceptualized to represent recognition of statistical structure rather than recognition of items retrieved from memory as whole constituents. We found that participants are equally sensitive to both types of learning products, yet subject them to varying degrees of conscious processing during the post-familiarization recognition test. The data point to the contribution of conscious awareness to at least some types of statistical learning content.
... At the broadest level, they confirm the conclusion that people attend to the property of boundedness (i.e. the internal temporal contour of events) when assembling representations of dynamically unfolding experience. Thus the present data contribute to growing evidence that the units of event cognition are themselves quite abstract (Ji & Papafragou, 2020, in press;; see also Hard et al., 2011;Hard et al., 2019;Loucks et al., 2017). Furthermore, by showing that the notion of boundedness that was inspired by treatments of linguistic meaning extends to properties of events in conceptual structure, our data indirectly support the presence of parallels between event language and event perception (see also Brockhoff et al., 2016;Folli & Harley, 2006;Hafri et al., 2013;Lakusta & Landau, 2005;Malaia, 2014;Papafragou, 2015;Tversky et al., 2011). ...
Events unfold over time, i.e. they have a beginning and endpoint. Previous studies have illustrated the importance of endpoints for event perception and memory. However, this work has only discussed events with a self-evident endpoint, and the internal temporal structure of events has not received much attention. In this study, we hypothesise that event cognition computes boundedness, an abstract feature of the internal temporal structure of events. We further hypothesise that sensitivity to boundedness affects how individual temporal slices of events (such as event midpoints or endpoints) are processed and integrated into a coherent event representation. The results of three experiments confirm these hypotheses. In Experiment 1, viewers identified the class of bounded (non-homogeneous, culminating) and unbounded (homogeneous, non-culminating) events in a categorisation task. In Experiments 2 and 3, viewers reacted differently to temporal disruptions in bounded versus unbounded events. We conclude that boundedness shapes how events are temporally processed.
... This could arise from a sort of "pop-out" effect where unexpected stimuli (or parts of stimuli) provoked greater levels of attention (Kristjánsson, Vuilleumier, Schwartz, Macaluso, & Driver, 2007). This is consistent with recent work showing that attention reorganizes as statistical structure in input sequences is learned (Hard, Meyer, & Baldwin, 2018; Zhao, Al-Aidroos, & Turk-Browne, 2013). Another function attributed to the angular gyrus is memory encoding 3 Three participants did not have OSpan data due to failure of the Eprime program used to present stimuli, leaving 18 participants for analyses with the OSpan. ...
... And far from being arbitrary or idiosyncratic, people tend to agree on when discrete events start and end, even in naturalistic stimuli (e.g., Zacks et al., 2009). Moreover, event boundaries loom large for other mental processes such as attention and memory (e.g., Dubrow & Davachi, 2016;Hard et al., 2019;Heusser et al., 2018;Huff et al., 2012;Radvansky, 2012;Swallow et al., 2009). ...
One of the most fundamental questions that can be asked about any process concerns the underlying units over which it operates. And this is true not just for artificial processes (such as functions in a computer program that only take specific kinds of arguments) but for mental processes. Over what units does the process of enumeration operate? Recent work has demonstrated that in visuospatial arrays, these units are often irresistibly discrete objects. When enumerating the number of discs in a display, for example, observers underestimate to a greater degree when the discs are spatially segmented (e.g., by connecting pairs of discs with lines): you try to enumerate discs, but your mind can't help enumerating dumbbells. This phenomenon has previously been limited to static displays, but of course our experience of the world is inherently dynamic. Is enumeration in time similarly based on discrete events? To find out, we had observers enumerate the number of notes in quick musical sequences. Observers underestimated to a greater degree when the notes were temporally segmented (into discrete musical phrases, based on pitch-range shifts), even while carefully controlling for both duration and the overall range and heterogeneity of pitches. Observers tried to enumerate notes, but their minds couldn't help enumerating musical phrases - since those are the events they experienced. These results thus demonstrate how discrete events are prominent in our mental lives, and how the units that constitute discrete events are not entirely under our conscious, intentional control.
The human mind automatically divides continuous experience into meaningful events (event segmentation). Despite abundant evidence that some kinds of situation changes (e.g., action, goal, or location changes) contribute to event segmentation, a component of experience that is critical for understanding and predicting others’ behavior, emotion, is rarely investigated. In two experiments, we sought to establish that viewers can track emotion changes while viewing naturalistic videos and that these changes contribute to event segmentation. Participants watched commercial film excerpts while identifying either emotion changes or event boundaries (moments that separate two events) of different grains (Experiment 1: neutral grain; Experiment 2: fine grain or coarse grain). We found that participants agreed with each other about when emotion changes occurred in the videos, demonstrating that viewers are able to track changes in the emotional content of dynamic naturalistic videos as they are experienced. Moreover, the emotion changes participants identified were temporally aligned with the event boundaries identified by other groups. In addition, valence and arousal changes rated by a separate group of participants uniquely predicted the likelihood of identifying emotion changes and event boundaries, even after accounting for other types of change. However, emotion changes were more strongly tied to valence changes than arousal changes while coarse boundaries were more strongly associated with affective changes than were fine boundaries. These novel findings suggest that emotional information plays a substantial role in structuring ongoing experiences into meaningful events, providing a stronger basis for understanding how emotion shapes the perception and memory of everyday experiences.
People readily imagine narratives in response to instrumental music. Although previous work has established that these narratives show broad intersubjectivity, it remains unclear whether these imagined stories are atemporal, or unfold systematically over the temporal extent of a musical excerpt. To investigate the dynamics of perceived musical narrative, we had participants first listen to 16 instrumental musical excerpts, which had previously been normed for factors of interest. While listening, participants continuously moved a slider to indicate their fluctuating perceptions of tension and relaxation. In a separate experimental session, participants reported the stories they imagined while listening to each excerpt, and then, while listening to the excerpts a final time, clicked a mouse to mark the time points at which they imagined new events in the ongoing imagined story. The time points of these event markings were not uniformly distributed throughout the excerpts, but were clustered at distinct moments, indicating that imagined narratives unfold in real time and entail general consensus about when listeners imagine events in the music. Moreover, the time points at which people tended to imagine events were correlated with the time points at which people tended to perceive salient changes in musical tension, as separately recorded within the first experimental session. The degree of alignment was greater for excerpts high in narrativity than those low in narrativity. Together, these results show that music can dynamically guide a listener's imagination and there is remarkable intersubjectivity in ‘when’ hear imagined story events in a piece of music.
In Basque–Spanish bilinguals, statistical learning (SL) in the visual modality was more efficient on nonlinguistic than linguistic input; in the auditory modality, we found the reverse pattern of results. We hypothesize that SL was shaped for processing nonlinguistic environmental stimuli and only later, as the language faculty emerged, recycled for speech processing. This led to further adaptive changes in the neurocognitive mechanisms underlying speech processing, including SL. By contrast, as a recent cultural innovation, written language has not yet led to adaptations. The current study investigated whether such phylogenetic influences on SL can be modulated by ontogenetic influences on a shorter timescale, over the course of individual development. We explored how SL is modulated by the ambient linguistic environment. We found that SL in the auditory modality can be further modulated by exposure to a bilingual environment, in which speakers need to process a wider range of diverse speech cues. This effect was observed only on linguistic, not nonlinguistic, material. We conclude that ontogenetic factors modulate the efficiency of already existing SL ability, honing it for specific types of input, by providing new targets for selection via exposure to different cues in the sensory input.
Two classes of cognitive mechanisms have been proposed to explain segmentation of continuous sensory input into discrete recurrent constituents: clustering and boundary-finding mechanisms. Clustering mechanisms are based on identifying frequently co-occurring elements and merging them together as parts that form a single constituent. Bracketing (or boundary-finding) mechanisms work by identifying rarely co-occurring elements that correspond to the boundaries between discrete constituents. In a series of behavioral experiments, I tested which mechanisms are at play in the visual modality both during segmentation of a continuous syllabic sequence into discrete word-like constituents and during recognition of segmented constituents. Additionally, I explored conscious awareness of the products of statistical learning—whole constituents versus merged clusters of smaller subunits. My results suggest that both online segmentation and offline recognition of extracted constituents rely on detecting frequently co-occurring elements, a process likely based on associative memory. However, people are more aware of having learnt whole tokens than of recurrent composite clusters.
Knowledge on statistical learning (SL) in healthy elderly is scarce. Theoretically, it is not clear whether aging affects modality-specific and/or domain-general learning mechanisms. Practically, there is a lack of research on simplified SL tasks, which would ease the burden of testing in clinical populations. Against this background, we conducted two experiments across three modalities (auditory, visual and visuomotor) in a total of 93 younger and older adults. In Experiment 1, SL was induced in all modalities. Aging effects appeared in the tasks relying on an explicit posttest to assess SL. We hypothesize that declines in domain-general processes that predominantly modulate explicit learning mechanisms underlie these aging effects. In Experiment 2, more feasible tasks were developed for which the level of SL was maintained in all modalities, except the auditory modality. These tasks are more likely to successfully measure SL in elderly (patient) populations in which task demands can be problematic.
Anticipating the future is essential for efficient perception and action planning. Yet, the role of anticipation in event segmentation is understudied because empirical research has focused on retrospective cues such as surprise. We address this question in the context of musical phrase-boundary perception. A computational model of cognitive sequence processing was used to control the information-dynamic properties of tone sequences. In an implicit, self-paced listening task (n=38), undergraduates dwelled longer on tones generating high entropy (i.e., high uncertainty) than those generating low entropy (i.e., low uncertainty). Similarly, sequences that ended on tones generating high entropy were rated as sounding more complete (n=31). These entropy effects were independent of both the surprise (i.e., information content) and phrase position of target tones in the original musical stimuli. Our results indicate that events generating high entropy prospectively contribute to segmentation processes in auditory sequence perception, independent of the properties of the subsequent event.
Among cognitive processes, although literally based on logically false propositions, analogical and metaphorical reasoning are the most used and useful thinking for communicating, understanding, discovering, problem-solving and learning. The topic of this chapter about analogy and metaphor, is to address the kind of computation linking a target category to a source category that belongs to another domain that might be able to support reasoning properties based on the fallacy of the falsity of propositions, on imperfection, imprecision and approxima-tion, gradualness, vagueness, fuzziness, uncertainty and implicit plausibility of likeness. Because notable advances in the computation of analogies are from Bernadette Bouchon-Meunier's work with her team: the fuzzy logic computation of analogical reasoning and schemes, we examine how such modeling of the hu-man computation of analogies can be used in turn to model the machine computa-tion of analogies.
Our brain receives a dynamically changing stream of sensorimotor data. Yet, we perceive a rather organized world, which we segment into and perceive as events. Computational theories of cognitive science on event-predictive cognition suggest that our brain forms generative, event-predictive models by segmenting sensorimotor data into suitable chunks of contextual experiences. Here, we introduce a hierarchical, surprise-gated recurrent neural network architecture, which models this process and develops compact compressions of distinct event-like contexts. The architecture contains a contextual LSTM layer, which develops generative compressions of ongoing and subsequent contexts. These compressions are passed to a GRU-like layer, which uses surprise signals to update its recurrent latent state. The latent state is passed on to another LSTM layer, which processes actual dynamic sensory flow in the light of the provided latent, contextual compression signals. Our model develops distinct event compressions and achieves the best performance on multiple event processing tasks. The architecture may be very useful for the further development of resource-efficient learning, hierarchical model-based reinforcement learning, as well as the development of artificial event-predictive cognition and intelligence.
To make sense of the world around us, we must be able to segment a continual stream of sensory inputs into discrete events. In this review, I propose that in order to comprehend events, we engage hierarchical generative models that "reverse engineer" the intentions of other agents as they produce sequential action in real time. By generating probabilistic predictions for upcoming events, generative models ensure that we are able to keep up with the rapid pace at which perceptual inputs unfold. By tracking our certainty about other agents' goals and the magnitude of prediction errors at multiple temporal scales, generative models enable us to detect event boundaries by inferring when a goal has changed. Moreover, by adapting flexibly to the broader dynamics of the environment and our own comprehension goals, generative models allow us to optimally allocate limited resources. Finally, I argue that we use generative models not only to comprehend events but also to produce events (carry out goal-relevant sequential action) and to continually learn about new events from our surroundings. Taken together, this hierarchical generative framework provides new insights into how the human brain processes events so effortlessly while highlighting the fundamental links between event comprehension, production, and learning.
When organizational members violate important organizational standards, they may face termination, or they may instead be retained by the organization and given a second chance. Retained transgressors experience the tension of liminality: they maintain their affiliation to the organization, making them structural insiders, but they have committed a transgression, making them moral outsiders. How might transgressors attempt to reintegrate and feel like full organizational insiders once again? And what makes transgressors feel more or less reintegrated? Previous work has studied reintegration from victims’ or third-parties’ perspectives, but little is known about transgressor reintegration. To build theory on transgressor reintegration, I studied transgressors at a military service academy. Through waves of qualitative data collection and inductive analyses, I find that transgressions threaten transgressors’ integration, leading transgressors to feel precarious in their perceptions of membership and their feelings of belonging. Transgressors attempt to restore both elements, but use distinct approaches for each. Because belonging restoration requires positive interactions with many organizational actors, transgressors can – and in my data, frequently do – experience restoration of membership but not belonging. Therefore it may be relatively rare for transgressors to feel highly reintegrated following transgressions, even in organizations that devote considerable resources to reintegration.
ABSTRACT
Second language teaching approaches that were originally proposed for
classroom context is used for blended learning context as well as
distance learning contexts regardless of their fundamental differences in
terms of applicability, accessibility, objectives, designs and theoretical
bases. Such theoretical and practical incompatibilities served as the
the rationale to bridge the gap of a basically devised second language (L2)
teaching approach for blended and distance learning contexts. The
proposed computer-assisted nonlinear dynamic approach (CANDA)
enables L2 learners and teachers to improve psychological factors and
communicative skills by suggesting the use of a variety of technology-based
apps for a multitude of learning styles. The CANDA, as an L2
teaching approach has significant theoretical and pedagogical
implications for educational app designers, computer-assisted teaching
programmers, blended and distance learning researchers and officials.
Events-the experiences we think we are having and recall having had-are constructed; they are not what actually occurs. What occurs is ongoing dynamic, multidimensional, sensory flow, which is somehow transformed via psychological processes into structured, describable, memorable units of experience. But what is the nature of the redescription processes that fluently render dynamic sensory streams as event representations? How do such processes cope with the ubiquitous novelty and variability that characterize sensory experience? How are event-rendering skills acquired and how do event representations change with development? This review considers emerging answers to these questions, beginning with evidence that an implicit tendency to monitor predictability structure via statistical learning is key to event rendering. That is, one way that the experience of bounded events (e.g., actions within behavior, words within speech) arises is with the detection of "troughs" in sensory predictability. Interestingly, such troughs in predictability are often predictable; these regions of predictable-unpredictability provide articulation points to demarcate one event from another in representations derived from the actual streaming information. In our information-optimization account, a fluent event-processor predicts such troughs and selectively attends to them-while suppressing attention to other regions-as sensory streams unfold. In this way, usage of attentional resources is optimized for efficient sampling of the most relevant, information-rich portions of the unfolding flow of sensation. Such findings point to the development of event-processing fluency-whether in action, language, or other domains-depending crucially on rapid and continual cognitive reorganization. As knowledge of predictability grows, attention is adaptively redeployed. Accordingly, event experiences undergo continuous alteration.
Although the stream of information we encounter is continuous, our experiences tend to be discretized into meaningful clusters, altering how we represent our past. Event segmentation theory proposes that clustering ongoing experience in this way is adaptive in that it promotes efficient online processing as well as later reconstruction of relevant information. A growing literature supports this theory by demonstrating its important behavioral consequences. Yet the exact mechanisms of segmentation remain elusive. Here, we provide a brief overview of how event segmentation influences ongoing processing, subsequent memory retrieval, and decision making as well as some proposed underlying mechanisms. We then explore how beliefs, or inferences, about what generates our experience may be the foundation of event cognition. In this inference‐based framework, experiences are grouped together according to what is inferred to have generated them. Segmentation then occurs when the inference changes, creating an event boundary. This offers an alternative to dominant theories of event segmentation, allowing boundaries to occur independent of perceptual change and even when transitions are predictable. We describe how this framework can reconcile seemingly contradictory empirical findings (e.g., memory can be biased toward both extreme episodes and the average of episodes). Finally, we discuss open questions regarding how time is incorporated into the inference process.
Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.
Statistical learning is a set of cognitive mechanisms allowing for extracting regularities from the environment and segmenting continuous sensory input into discrete units. The current study used functional magnetic resonance imaging (fMRI) (N = 25) in conjunction with an artificial language learning paradigm to provide new insight into the neural mechanisms of statistical learning, considering both the online process of extracting statistical regularities and the subsequent offline recognition of learned patterns. Notably, prior fMRI studies on statistical learning have not contrasted neural activation during the learning and recognition experimental phases. Here, we found that learning is supported by the superior temporal gyrus and the anterior cingulate gyrus, while subsequent recognition relied on the left inferior frontal gyrus. Besides, prior studies only assessed the brain response during the recognition of trained words relative to novel nonwords. Hence, a further key goal of this study was to understand how the brain supports recognition of discrete constituents from the continuous input versus recognition of mere statistical structure that is used to build new constituents that are statistically congruent with the ones from the input. Behaviorally, recognition performance indicated that statistically congruent novel tokens were less likely to be endorsed as parts of the familiar environment than discrete constituents. fMRI data showed that the left intraparietal sulcus and angular gyrus support the recognition of old discrete constituents relative to novel statistically congruent items, likely reflecting an additional contribution from memory representations for trained items.
For working memory to be efficient, it is important not only to remember, but also to forget-thus freeing up memory for additional information. But what triggers forgetting? Beyond continuous temporal decay, memory is thought to be effectively 'flushed' to some degree at discrete event boundaries-i.e. when one event ends and another begins. But this framework does not readily apply to real-world visual experience, where events are constantly and asynchronously beginning, unfolding, and ending all around us. In this rush of things always happening, when might memory be flushed? In a series of experiments, we explored this using maximally simple visual events. A number of dots appeared, a subset moved at random speeds in random directions, and observers simply had to estimate the number of dots that moved. Critically, however, these motions could begin and end asynchronously. In general, asynchronous motions led to underestimation, but further experiments demonstrated that this was driven only by endings: regardless of whether dots started moving together or separately, animations with asynchronous endings led to underestimation-even while carefully controlling for both the overall amount of motion and average starting and ending times. (In contrast, no such effect occurred for asynchronous beginnings.) Thus, the ends of events seem to have an outsize influence on working memory-but only in the context of other ongoing events: once a motion ends amidst other unfinished motions, it seems more difficult to recall that particular motion as having occurred as a distinct event.
Fluent event processing involves selectively attending to information-rich regions within dynamically unfolding sensory streams (e.g., Newtson, 1973). What counts as information-rich likely depends on numerous factors, however, including overall event novelty and local opportunity for repeated viewing. Using Hard, Recchia, and Tversky's (2011) method, we investigated the extent to which these two variables affected viewers' attentional patterns as events unfolded. Specifically, we recorded viewers' "dwell times" as they advanced through two slideshows depicting distinct methods of shoelace tying varying in novelty but equated on other dimensions. Across two experiments, novelty sparked increased dwelling overall, and viewers' dwelling patterns displayed rapid and systematic reorganization to structure within the activity stream after just one viewing of distinctively novel content. As well, increased dwelling positively predicted memory performance. These findings newly illuminate reorganization in attention as relevant information within novel activity sequences is quickly incorporated to guide event processing and support event memory.
How do people perceive routine events, such as making a bed, as these events unfold in time? Research on knowledge structures suggests that people conceive of events as goal-directed partonomic hierarchies. Here, participants segmented videos of events into coarse and fine units on separate viewings; some described the activity of each unit as well. Both segmentation and descriptions support the hierarchical bias hypothesis in event perception: Observers spontaneously encoded the events in terms of partonomic hierarchies. Hierarchical organization was strengthened by simultaneous description and, to a weaker extent, by familiarity. Describing from memory rather than perception yielded fewer units but did not alter the qualitative nature of the descriptions. Although the descriptions were telegraphic and without communicative intent, their hierarchical structure was evident to naive readers. The data suggest that cognitive schemata mediate between perceptual and functional information about events and indicate that these knowledge structures may be organized around object/action units.
Abstract Everyday experience consists of rapidly unfolding sensory information that humans redescribe as discrete events. Quick and efficient redescription facilitates remembering, responding to, and learning from the ongoing sensory flux. Segmentation seems key to successful redescription: the extent to which viewers can identify boundaries between event units within continuously unfolding activities predicts both memory and action performance. However, what happens to processing when boundary content is missing? Events occurring in naturalistic situations seldom receive continuous undivided attention. As a consequence, information, including boundary content, is likely sometimes missed. In this research, we systematically explored the influence of missing information by asking participants to advance at their own pace through a series of slideshows. Some slideshows, while otherwise matched in content, contained just half of the slides present in other slideshows. Missing content sometimes occurred at boundaries. As it turned out, patterns of attention during slideshow viewing were strikingly similar across matched slideshows despite missing content, even when missing content occurred at boundaries. Moreover, to the extent that viewers compensated with increased attention, missing content did not significantly undercut event recall. These findings seem to further confirm an information optimization account of event processing: event boundaries receive heightened attention because they forecast unpredictability and thus, optimize the uptake of new information. Missing boundary content sparks little change in patterns of attentional modulation, presumably because the underlying predictability parameters of the unfolding activity itself are unchanged by missing content. Optimizing information, thus, enables event processing and recall to be impressively resilient to missing content.
Previous research has shown how spatial attention is guided to a target location, but little is understood about how attention is allocated to an event in time. The authors introduce a paradigm to manipulate the sequential structure of visual events independent of responses. They asked whether this temporal context could be implicitly learned and used to guide attention to a relative point in time or location, or both, in space. Experiments show that sequentially structured event durations, event identities, and spatiotemporal event sequences can guide attention to a point in time as well as to a target event's identity and location. Cuing was found to rely heavily on the element immediately preceding the target, although cuing from earlier items also was evident. Learning was implicit in all cases. These results show that the sequential structure of the visual world plays an important role in guiding visual attention to target events.
How do people understand the everyday, yet intricate, behaviors that unfold around them? In the present research, we explored this by presenting viewers with self-paced slideshows of everyday activities and recording looking times, subjective segmentation (breakpoints) into action units, and slide-to-slide physical change. A detailed comparison of the joint time courses of these variables showed that looking time and physical change were locally maximal at breakpoints and greater for higher level action units than for lower level units. Even when slideshows were scrambled, breakpoints were regarded longer and were more physically different from ordinary moments, showing that breakpoints are distinct even out of context. Breakpoints are bridges: from one action to another, from one level to another, and from perception to conception.
Memory is fleeting. New material rapidly obliterates previous material. How then can the brain deal successfully with the continual deluge of linguistic input? We argue that, to deal with this “Now-or-Never” bottleneck, the brain must compress and recode linguistic input as rapidly as possible. This observation has strong implications for the nature of language processing: (i) the language system must “eagerly” recode and compress linguistic input; (ii) as the bottleneck recurs at each new representational level, the language system must build a multi-level linguistic representation; and (iii) it must deploy all available information predictively to ensure that local linguistic ambiguities are dealt with “Right-First-Time;” once the original input is lost, there is no way for the language system to recover. This is “Chunk-and-Pass” processing. Similarly, language
learning
must also occur in the here-and-now. This implies that language acquisition is learning to process, rather than inducing a grammar. Moreover, this perspective provides a cognitive foundation for grammaticalization and other aspects of language change. Chunk-and-Pass processing also helps explain a variety of core properties of language, including its multi-level representational structure and duality of patterning. This approach promises to create a direct relationship between psycholinguistics and linguistic theory. More generally, we outline a framework within which to integrate often disconnected inquiries into language processing, language acquisition, and language change and evolution.
Conducted 2 analyses from a study by D. Newtson et al (1976) of the relation between the segmentation of 7 ongoing behavior sequences into their component actions and the movement in those sequences. The 1st analysis confirmed that action-unit boundaries consist of stimulus points depicting distinctive changes relative to the previously used action-unit boundary, rather than consisting of distinctive, action-defining states. The 2nd analysis tested more rigorously the notion that distinctive changes form the objective basis of behavior units by examining the transitions between stimulus points within action units and transitions to and from action-unit boundaries; results of this analysis also support a distinctive-change interpretation. Results of previous studies of action perception are reviewed, and a preliminary hypothesis as to the nature of behavior perception processes is presented and discussed. (19 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Infants must learn about many cognitive domains (e.g., language, music) from auditory statistics, yet capacity limits on their cognitive resources restrict the quantity that they can encode. Previous research has established that infants can attend to only a subset of available acoustic input. Yet few previous studies have directly examined infant auditory attention, and none have directly tested theorized mechanisms of attentional selection based on stimulus complexity. This work utilizes model-based behavioral methods that were recently developed to examine visual attention in infants (e.g., Kidd, Piantadosi, & Aslin, 2012). The present results demonstrate that 7- to 8-month-old infants selectively attend to nonsocial auditory stimuli that are intermediately predictable/complex with respect to their current implicit beliefs and expectations. These findings provide evidence of a broad principle of infant attention across modalities and suggest that sound-to-sound transitional statistics heavily influence the allocation of auditory attention in human infants.
Two experiments investigated whether infants can use their rich social knowledge to bind representations of individual objects into larger social units, thereby overcoming the three-item limit of working memory. In Experiment 1, 16-month-olds (n = 32) remembered up to four hidden dolls when the dolls had faced and interacted with each other in pairs, but not when they faced and interacted with the infant, suggesting that infants chunked the dolls into social pairs. In Experiment 2 (n = 16), infants failed to remember four dolls when they faced each other without interacting, indicating that interaction between the dolls was necessary to drive chunking. This work bridges a gap between social cognition and memory by demonstrating that infants can use social cues to expand memory.
Boundaries between meaningful events are key moments in comprehending human action. At these points, viewers may focus on the event's contents at the expense of other information. We tested whether visual detection was impaired at those moments perceivers judged to be boundaries between events. Short animated football clips were used as stimulus material, and event boundaries were imposed by having the ball change possession. In a first experiment, we found that possession changes were perceived to be event boundaries. In a second experiment, participants were asked to keep track of 4 of 10 players and to watch for 120 ms probes appearing either at an event boundary or a nonboundary. Probe detection was less accurate at event boundaries. This result suggests that the segmentation of ongoing activity into events corresponds with the regulation of attention over time.
Tested implications for attribution processes of variation in the unit of perception in 2 experiments with college freshmen males (n = 20). In Exp I Ss viewed a 5-min videotaped behavior sequence. Ss were instructed to segment the behavior into as fine units of action or as gross units of action as were natural and meaningful to them. Results indicate that in comparison to gross-unit Ss, fine-unit Ss were more confident in their impressions, made more dispositional attributions, and tended to have more differentiated impressions. In Exp II Ss viewed either of 2 comparable sequences of problem-solving behavior; in 1, an unexpected action was inserted. Following the unexpected act, Ss employed more units of perception/min than controls who did not view it. It is concluded that the unit of perception varies according to situational constraints and that attribution theories assuming constant units are seriously in error. Implications of unit variation for the interpretation of attribution research are discussed. (17 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Everyday events, such as making a bed, can be segmented hierarchically, with the coarse level characterized by changes in the actor's goals and the fine level by subgoals (Zacks, Tversky, & Iyer, 2001). Does hierarchical event perception depend on knowledge of actors' intentions? This question was addressed by asking participants to segment films of abstract, schematic events. Films were novel or familiarized, viewed forward or backward, and simultaneously described or not. The participants interpreted familiar films as more intentional than novel films and forward films as more intentional than backward films. Regardless of experience and film direction, however, the participants identified similar event boundaries and organized them hierarchically. An analysis of the movements in each frame revealed that event segments corresponded to bursts of change in movement features, with greater bursts for coarse than for fine units. Perceiving event structure appears to enable event schemas, rather than resulting from them.
One of the infant's first tasks in language acquisition is to discover the words embedded in a mostly continuous speech stream. This learning problem might be solved by using distributional cues to word boundaries—for example, by computing the transitional probabilities between sounds in the language input and using the relative strengths of these probabilities to hypothesize word boundaries. The learner might be further aided by language-specific prosodic cues correlated with word boundaries. As a first step in testing these hypotheses, we briefly exposed adults to an artificial language in which the only cues available for word segmentation were the transitional probabilities between syllables. Subjects were able to learn the words of this language. Furthermore, the addition of certain prosodic cues served to enhance performance. These results suggest that distributional cues may play an important role in the initial word segmentation of language learners.
Human infants, like immature members of any species, must be highly selective in sampling information from their environment to learn efficiently. Failure to be selective would waste precious computational resources on material that is already known (too simple) or unknowable (too complex). In two experiments with 7- and 8-month-olds, we measure infants' visual attention to sequences of events varying in complexity, as determined by an ideal learner model. Infants' probability of looking away was greatest on stimulus items whose complexity (negative log probability) according to the model was either very low or very high. These results suggest a principle of infant attention that may have broad applicability: infants implicitly seek to maintain intermediate rates of information absorption and avoid wasting cognitive resources on overly simple or overly complex events.
Little is known about the brain mechanisms involved in word learning during infancy and in second language acquisition and about the way these new words become stable representations that sustain language processing. In several studies we have adopted the human simulation perspective, studying the effects of brain-lesions and combining different neuroimaging techniques such as event-related potentials and functional magnetic resonance imaging in order to examine the language learning (LL) process. In the present article, we review this evidence focusing on how different brain signatures relate to (i) the extraction of words from speech, (ii) the discovery of their embedded grammatical structure, and (iii) how meaning derived from verbal contexts can inform us about the cognitive mechanisms underlying the learning process. We compile these findings and frame them into an integrative neurophysiological model that tries to delineate the major neural networks that might be involved in the initial stages of LL. Finally, we propose that LL simulations can help us to understand natural language processing and how the recovery from language disorders in infants and adults can be accomplished.
Memory for naturalistic events over short delays is important for visual scene processing, reading comprehension, and social interaction. The research presented here examined relations between how an ongoing activity is perceptually segmented into events and how those events are remembered a few seconds later. In several studies, participants watched movie clips that presented objects in the context of goal-directed activities. Five seconds after an object was presented, the clip paused for a recognition test. Performance on the recognition test depended on the occurrence of perceptual event boundaries. Objects that were present when an event boundary occurred were better recognized than other objects, suggesting that event boundaries structure the contents of memory. This effect was strongest when an object's type was tested but was also observed for objects' perceptual features. Memory also depended on whether an event boundary occurred between presentation and test; this variable produced complex interactive effects that suggested that the contents of memory are updated at event boundaries. These data indicate that perceptual event boundaries have immediate consequences for what, when, and how easily information can be remembered.
Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during statistical learning to explore these questions. Participants viewed statistically structured versus unstructured sequences of shapes while performing a task unrelated to the structure. Robust neural responses to statistical structure were observed, and these responses were notable in four ways: First, responses to structure were observed in the striatum and medial temporal lobe, suggesting that statistical learning may be related to other forms of associative learning and relational memory. Second, statistical regularities yielded greater activation in category-specific visual regions (object-selective lateral occipital cortex and word-selective ventral occipito-temporal cortex), demonstrating that these regions are sensitive to information distributed in time. Third, evidence of learning emerged early during familiarization, showing that statistical learning can operate very quickly and with little exposure. Finally, neural signatures of learning were dissociable from subsequent explicit familiarity, suggesting that learning can occur in the absence of awareness. Overall, our findings help elucidate the underlying nature of statistical learning.
Pioneering work in the 1940s and 1950s suggested that the concept of 'chunking' might be important in many processes of perception, learning and cognition in humans and animals. We summarize here the major sources of evidence for chunking mechanisms, and consider how such mechanisms have been implemented in computational models of the learning process. We distinguish two forms of chunking: the first deliberate, under strategic control, and goal-oriented; the second automatic, continuous, and linked to perceptual processes. Recent work with discrimination-network computational models of long- and short-term memory (EPAM/CHREST) has produced a diverse range of applications of perceptual chunking. We focus on recent successes in verbal learning, expert memory, language acquisition and learning multiple representations, to illustrate the implementation and use of chunking mechanisms within contemporary models of human learning.
Experience plays a crucial role for the normal development of many perceptual and cognitive functions, such as speech perception. For example, between 6 and 10 months of age, the infant's ability to discriminate among native speech sounds improves, whereas the ability to discriminate among foreign speech sounds declines. However, a recent investigation suggests that some experience with nonnative languages from 9 months of age facilitates the maintenance of this ability at 12 months. Nelson has suggested that the systems underlying face processing may be similarly sculpted by experience with different kinds of faces. In the current investigation, we demonstrate that, in human infants between 6 and 9 months of age, exposure to nonnative faces, in this case, faces of Barbary macaques (Macaca sylvanus), facilitates the discrimination of monkey faces, an ability that is otherwise lost around 9 months of age. These data support, and further elucidate, the role of early experience in the development of face processing.
• development
• early experience
• monkey
• recognition
The visual environment contains massive amounts of information involving the relations between objects in space and time, and recent studies of visual statistical learning (VSL) have suggested that this information can be automatically extracted by the visual system. The experiments reported in this article explore the automaticity of VSL in several ways, using both explicit familiarity and implicit response-time measures. The results demonstrate that (a) the input to VSL is gated by selective attention, (b) VSL is nevertheless an implicit process because it operates during a cover task and without awareness of the underlying statistical patterns, and (c) VSL constructs abstracted representations that are then invariant to changes in extraneous surface features. These results fuel the conclusion that VSL both is and is not automatic: It requires attention to select the relevant population of stimuli, but the resulting learning then occurs without intent or awareness.
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.
An argument that the way we listen to speech is shaped by our experience with our native language.
Understanding speech in our native tongue seems natural and effortless; listening to speech in a nonnative language is a different experience. In this book, Anne Cutler argues that listening to speech is a process of native listening because so much of it is exquisitely tailored to the requirements of the native language. Her cross-linguistic study (drawing on experimental work in languages that range from English and Dutch to Chinese and Japanese) documents what is universal and what is language specific in the way we listen to spoken language.
Cutler describes the formidable range of mental tasks we carry out, all at once, with astonishing speed and accuracy, when we listen. These include evaluating probabilities arising from the structure of the native vocabulary, tracking information to locate the boundaries between words, paying attention to the way the words are pronounced, and assessing not only the sounds of speech but prosodic information that spans sequences of sounds. She describes infant speech perception, the consequences of language-specific specialization for listening to other languages, the flexibility and adaptability of listening (to our native languages), and how language-specificity and universality fit together in our language processing system.
Drawing on her four decades of work as a psycholinguist, Cutler documents the recent growth in our knowledge about how spoken-word recognition works and the role of language structure in this process. Her book is a significant contribution to a vibrant and rapidly developing field.
Fluent event processing involves selectively attending to information-rich regions within dynamically unfolding sensory streams (e.g., Newtson, 1973). What counts as information-rich likely depends on numerous factors, however, including overall event novelty and local opportunity for repeated viewing. Using Hard, Recchia, and Tversky's (2011) method, we investigated the extent to which these two variables affected viewers' attentional patterns as events unfolded. Specifically, we recorded viewers' "dwell times" as they advanced through two slideshows depicting distinct methods of shoelace tying varying in novelty but equated on other dimensions. Across two experiments, novelty sparked increased dwelling overall, and viewers' dwelling patterns displayed rapid and systematic reorganization to structure within the activity stream after just one viewing of distinctively novel content. As well, increased dwelling positively predicted memory performance. These findings newly illuminate reorganization in attention as relevant information within novel activity sequences is quickly incorporated to guide event processing and support event memory.
An argument that the way we listen to speech is shaped by our experience with our native language. © 2012 Massachusetts Institute of Technology. All rights reserved.
Throughout their 1st year, infants adeptly detect statistical structure in their environment. However, little is known about whether statistical learning is a primary mechanism for event segmentation. This study directly tests whether statistical learning alone is sufficient to segment continuous events. Twenty-eight 7- to 9-month-old infants viewed a sequence of continuous actions performed by a novel agent in which there were no transitional movements that could have constrained the possible upcoming actions. At test, infants distinguished statistically intact units from less predictable ones. The ability to segment events using statistical structure may help infants discover other cues to event boundaries, such as intentions, and carve up the world of continuous motion in meaningful ways.
What types of mechanisms underlie the acquisition of human language? Recent evidence suggests that learners, including infants, can use statistical properties of linguistic input to discover structure, including sound patterns, words, and the beginnings of grammar. These abilities appear to be both powerful and constrained, such that some statistical patterns are more readily detected and used than others. Implications for the structure of human languages are discussed.
Cognition depends on representations of the world that are much like descriptions. That is, processes of selection, organization, and categorization of the relations among things provide people with a view of the world that in principle could be different. A theoretical problem for a descriptional theory is the question of how the rich mapping of sensory detail available in perception is related to the descriptional character of information in higher level perception, memory, knowledge, and reasoning. In this paper, I review evidence in favour of a descriptional theory of cognition and suggest that work on change blindness clarifies the nature of the juncture between perception and cognition. In turn, work from the descriptional point of view clarifies the nature of change blindness. I discuss change blindness from this perspective for the topics of attention, recognition, and the adaptive use of information. I close with a discussion of new issues that are raised.
Twenty-one subjects were required to detect deletions of four, eight, or 12 frames from ongoing films. Deletions were within perceptual units of behavior (nonbreakpoints) or at the boundaries (breakpoints). Detection of deletions was more accurate at breakpoints than at nonbreakpoints, and increased in accuracy as a function of length of deletion for breakpoints only. In a second study, 79 subjects viewed triads of slides consisting of three consecutive breakpoints or nonbreakpoints. Order of presentation was also varied. Results indicated that subjects viewing breakpoints were (a) significantly more accurate in action descriptions, (b) rated the sequence as more intelligible, and (c) more accurately judged the correctness of slide ordering. A third study tested recognition for breakpoints and nonbreakpoints following viewing of six short films. Results indicated a small but highly significant and consistent superiority in recognition accuracy for breakpoint items. Signal detection analysis confirmed that these results were due to the increased discriminability of breakpoint items.
Bell System Technical Journal, also pp. 623-656 (October)
A quantitative measure of “information” is developed which is based on physical as contrasted with psychological considerations. How the rate of transmission of this information over a system is limited by the distortion resulting from storage of energy is discussed from the transient viewpoint. The relation between the transient and steady state viewpoints is reviewed. It is shown that when the storage of energy is used to restrict the steady state transmission to a limited range of frequencies the amount of information that can be transmitted is proportional to the product of the width of the frequency-range by the time it is available. Several illustrations of the application of this principle to practical systems are included. In the case of picture transmission and television the spacial variation of intensity is analyzed by a steady state method analogous to that commonly used for variations with time.
To understand the world around us, continuous streams of information including speech must be segmented into units that can be mapped onto stored representations. Recent evidence has shown that event-related potentials (ERPs) can index the online segmentation of sound streams. In the current study, listeners were trained to recognize sequences of three nonsense sounds that could not easily be rehearsed. Beginning 40 ms after onset, sequence-initial sounds elicited a larger amplitude negativity after compared to before training. This difference was not evident for medial or final sounds in the sequences. Across studies, ERP segmentation effects are remarkably similar regardless of the available segmentation cues and nature of the continuous streams. These results indicate the preferential processing of sequence-initial information is not domain specific and instead implicate a more general cognitive mechanism such as temporally selective attention.
How do learners discover the structure in linguistic input? One set of cues which learners might use to acquire phrase structure are the dependencies, or predictive relationships, which link elements within phrases. In order to determine whether learners can use this statistical information, adults and children were exposed to artificial languages that either contained or violated the kinds of dependencies that characterize natural languages. Additional experiments contrasted the acquisition of these linguistic systems with the same grammars implemented as non-linguistic input (sequences of nonlinguistic sounds or shapes). Predictive relationships yielded better learning for sequentially presented auditory stimuli, and for simultaneously presented visual stimuli, but no such advantage was found for sequentially presented visual stimuli. Learning outcomes were not affected by the degree to which the input contained linguistic content. These findings suggest that constraints on learning mechanisms that mirror the structure of natural languages are not tailored solely for language learning. Implications for theories of language acquisition and perceptual learning are discussed.
In order to understand ongoing activity, observers segment it into meaningful temporal parts. Segmentation can be based on bottom-up processing of distinctive sensory characteristics, such as movement features. Segmentation may also be affected by top-down effects of knowledge structures, including information about actors’ intentions. Three experiments investigated the role of movement features and intentions in perceptual event segmentation, using simple animations. In all conditions, movement features significantly predicted where participants segmented. This relationship was stronger when participants identified larger units than when they identified smaller units, and stronger when the animations were generated randomly than when they were generated by goal-directed human activity. This pattern suggests that bottom-up processing played an important role in segmentation of these stimuli, but that this was modulated by top-down influence of knowledge structures. To describe accurately how observers perceive ongoing activity, one must account for the effects of distinctive sensory characteristics, the effects of knowledge structures, and their interactions.
When we observe others in motion, we usually care little about the surface behaviors they exhibit. What matters are their underlying intentions. Judgments about intentions and intentionality dictate how we understand and remember others’ actions, how we respond, and what we predict about their future action. A generative knowledge system underlies our skill at discerning intentions, enabling us to comprehend intentions even when action is novel and unfolds in complex ways over time. Recent work spanning many disciplines illuminates some of the processes involved in intention detection. We review these developments and articulate a set of questions cutting across current theoretical dividing lines.
Making progress toward human-level artificial intelligence often seems to require a large number of difficult-to-integrale computational methods and enormous amounts of knowledge about the world. This article provides evidence from linguistics, cognitive psychology, and neuroscience for the cognitive substrate hypothesis that a relatively small set of properly integrated data structures and algorithms can underlie the whole range of cognition required for human-level intelligence. Some computational principles (embodied in the Polyscheme cognitive architecture) are proposed to solve the integration problems involved in implementing such a substrate. A natural language syntactic parser that uses only the mechanisms of an infant physical reasoning model developed in Polyscheme demonstrates that a single cognitive substrate can underlie intelligent systems in superficially very dissimilar domains. This work suggests that identifying and implementing a cognitive substrate will accelerate progress toward human-level artificial intelligence.
Event-related potential (ERP) evidence indicates that listeners selectively attend to word onsets in continuous speech, but the reason for this preferential processing is unknown. The current study measured ERPs elicited by syllable onsets in an artificial language to test the hypothesis that listeners direct attention to word onsets because their identity is unpredictable. Both before and after recognition training, participants listened to a continuous stream of six nonsense words arranged in pairs, such that the second word in each pair was completely predictable. After training, first words in pairs elicited a larger negativity beginning around 100 ms after onset. This effect was not evident for the completely predictable second words in pairs. These results suggest that listeners are most likely to attend to the segments in speech that they are least able to predict.
Identification of distinct units within a continuous flow of human action is fundamental to action processing. Such segmentation may rest in part on statistical learning. In a series of four experiments, we examined what types of statistics people can use to segment a continuous stream involving many brief, goal-directed action elements. The results of Experiment 1 showed no evidence for sensitivity to conditional probability, whereas Experiment 2 displayed learning based on joint probability. In Experiment 3, we demonstrated that additional exposure to the input failed to engender sensitivity to conditional probability. However, the results of Experiment 4 showed that a subset of adults-namely, those more successful at identifying actions that had been seen more frequently than comparison sequences-were also successful at learning conditional-probability statistics. These experiments help to clarify the mechanisms subserving processing of intentional action, and they highlight important differences from, as well as similarities to, prior studies of statistical learning in other domains, including language.
In an information-rich visual world and with limited attentional resources, what visual cues allow humans to efficiently navigate their environment? One key environmental characteristic is that stimuli rarely appear in isolation and typically coincide with other specific items that provide cues regarding where and when to guide our attention. Indeed, a predictive spatial context of distractors can enhance the deployment of attention to a target location (Chun & Jiang, 1998). However, can a predictive, temporal sequence of distractors, which do not enter working memory, cue when to allocate attention? Previous studies addressing this question have employed relatively long ( approximately < 500 msec/item) stimulus exposure durations. Thus, this temporal cuing may require extensive processing of the distractors. Here, we show that a rapidly presented (approximately 100 msec/item), predictive, temporal context, where stimuli undergo only preliminary analysis, can facilitate the deployment of attention to a specific temporal location.
Statistical learning has been studied as a mechanism by which people automatically and implicitly learn patterns in the environment. Here, we sought to examine general assumptions about statistical learning, including whether the learning is long-term, and whether it can occur implicitly. We exposed participants to a stream of stimuli, then tested them immediately after, or 24h after, exposure, with separate tests meant to measure implicit and explicit knowledge. To measure implicit learning, we analyzed reaction times during a rapid serial visual presentation detection task; for explicit learning, we used a matching questionnaire. Subjects' reaction time performance indicated that they did implicitly learn the exposed sequences, and furthermore, this learning was unrelated to explicit learning. These learning effects were observed both immediately after exposure and after a 24-h delay. These experiments offer concrete evidence that statistical learning is long-term and that the learning involves implicit learning mechanisms.
During perception, people segment continuous activity into discrete events. They do so in part by monitoring changes in features of an ongoing activity. Characterizing these features is important for theories of event perception and may be helpful for designing information systems. The three experiments reported here asked whether the body movements of an actor predict when viewers will perceive event boundaries. Body movements were recorded using a magnetic motion tracking system and compared with viewers' segmentation of his activity into events. Changes in movement features were strongly associated with segmentation. This was more true for fine-grained than for coarse-grained boundaries, and was strengthened when the stimulus displays were reduced from live-action movies to simplified animations. These results suggest that movement variables play an important role in the process of segmenting activity into meaningful events, and that the influence of movement on segmentation depends on the availability of other information sources.
Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the
environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance
of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation
of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between
neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure,
suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language
input.
The domain-general learning mechanisms elicited in incidental learning situations are of potential interest in many research fields, including language acquisition, object knowledge formation and motor learning. They have been the focus of studies on implicit learning for nearly 40 years. Stemming from a different research tradition, studies on statistical learning carried out in the past 10 years after the seminal studies by Saffran and collaborators, appear to be closely related, and the similarity between the two approaches is strengthened further by their recent evolution. However, implicit learning and statistical learning research favor different interpretations, focusing on the formation of chunks and statistical computations, respectively. We examine these differing approaches and suggest that this divergence opens up a major theoretical challenge for future studies.
Human social, cognitive, and linguistic functioning depends on skills for rapidly processing action. Identifying distinct acts within the dynamic motion flow is one basic component of action processing; for example, skill at segmenting action is foundational to action categorization, verb learning, and comprehension of novel action sequences. Yet little is currently known about mechanisms that may subserve action segmentation. The present research documents that adults can register statistical regularities providing clues to action segmentation. This finding provides new evidence that structural knowledge gained by mechanisms such as statistical learning can play a role in action segmentation, and highlights a striking parallel between processing of action and processing in other domains, such as language.
People make sense of continuous streams of observed behavior in part by segmenting them into events. Event segmentation seems to be an ongoing component of everyday perception. Events are segmented simultaneously at multiple timescales, and are grouped hierarchically. Activity in brain regions including the posterior temporal and parietal cortex and lateral frontal cortex increases transiently at event boundaries. The parsing of ongoing activity into events is related to the updating of working memory, to the contents of long-term memory, and to the learning of new procedures. Event segmentation might arise as a side effect of an adaptive mechanism that integrates information over the recent past to improve predictions about the near future.
Human activity contains sequential dependencies that observers may use to structure a task environment (e.g., the ordering of steps when tying shoes or getting into a car). Two experiments investigated how people take advantage of sequential structure to understand activity and respond to behaviorally relevant events. Participants monitored animations of simplified human movement to identify target hand gestures. In the first experiment, participants were able to use predictive sequential dependencies to more quickly identify targets. In addition, performance was best at the point in time that followed the sequence. However, the second experiment revealed that how sequential structure affects detection depends on whether the sequence predicts the timing of target events. In all cases, sequence learning was observed without participants' awareness of the sequential dependencies. These results suggest that human activity sequences can be learned without awareness and can be used to adaptively guide behavior.
Attentional enhancement at event boundaries. Poster presented at the meeting of the
- D Baldwin
- E Pederson
Baldwin, D., & Pederson, E. (2016). Attentional enhancement at event
boundaries. Poster presented at the meeting of the Cognitive
Science Society, Philadelphia, PA.
Discerning intentions: Characterizing the cognitive system at play
- D Baldwin
Baldwin, D. (2005). Discerning intentions: Characterizing the cognitive
system at play. In B. Homer & C. Tamis-LeMonda (Eds.), The
development of social cognition and communication (pp. 117-144). Mahwah, NJ: Erlbaum.
Attention rapidly reorganizes to structure in a novel activity sequence. Cognition. Accepted manuscript pending minor revision
- J Kosie
- D Baldwin
Kosie, J., & Baldwin, D. (2018a). Attention rapidly reorganizes to structure in a novel activity sequence. Cognition. Accepted manuscript
pending minor revision.