Statistical learning refers to the ability to pick up on the statistical regularities in our sensory environment, typically without intention or conscious awareness. Since the seminal publication on statistical learning in 1996, sensitivity to regularities has become a key concept in our understanding of language acquisition as well as other cognitive functions such as perception and attention.
Neuroimaging studies investigating which brain areas underpin statistical learning have mapped a network of domain-general regions in the medial temporal lobe as well as modality-specific regions in early sensory cortices. Research using electroencephalography has further demonstrated how sensitivity to structure impacts the brain’s processing of sensory input.
In response to concerns about the large discrepancy between the very simplistic artificial regularities employed in laboratory experiments on statistical learning and the much noisier and more complex regularities humans face in the real world, recent studies have taken more ecological approaches.
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Statistical Learning
Louisa Bogaerts, Noam Siegelman, and Ram Frost
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Influence of Anxiety on Cognitive Control Processes
DeMond M. Grant and Evan J. White
Cognitive control is the ability to direct attention and cognitive resources toward achieving one’s goals. However, research indicates that anxiety biases multiple cognitive processes, including cognitive control. This occurs in part because anxiety leads to excessive processing of threatening stimuli at the expense of ongoing activities. This enhanced processing of threat interferes with several cognitive processes, which includes how individuals view and respond to their environment. Specifically, research indicates that anxious individuals devote their attention toward threat when considering both early, automatic processes and later, sustained attention. In addition, anxiety has negative effects on working memory, which involves the ability to hold and manipulate information in one’s consciousness. Anxiety has been found to decrease the resources necessary for effective working memory performance, as well as increase the likelihood of negative information entering working memory. Finally, anxiety is characterized by focusing excessive attention on mistakes, and there is also a reduction in the cognitive control resources necessary to correct behavior. Enhancing our knowledge of how anxiety affects cognitive control has broad implications for understanding the development of anxiety disorders, as well as emerging treatments for these conditions.
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Working Memory
Tom Hartley and Graham J. Hitch
Working memory is an aspect of human memory that permits the maintenance and manipulation of temporary information in the service of goal-directed behavior. Its apparently inelastic capacity limits impose constraints on a huge range of activities from language learning to planning, problem-solving, and decision-making. A substantial body of empirical research has revealed reliable benchmark effects that extend to a wide range of different tasks and modalities. These effects support the view that working memory comprises distinct components responsible for attention-like control and for short-term storage. However, the nature of these components, their potential subdivision, and their interrelationships with long-term memory and other aspects of cognition, such as perception and action, remain controversial and are still under investigation. Although working memory has so far resisted theoretical consensus and even a clear-cut definition, research findings demonstrate its critical role in both enabling and limiting human cognition and behavior.
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Psychological Responses to Scarcity
Jiaying Zhao and Brandon M. Tomm
Scarcity is the condition of having insufficient resources to cope with demands. This condition presents significant challenges to the human cognitive system. For example, having limited financial resources requires the meticulous calculation of expenses with respect to a budget. Likewise, having limited time requires the stringent management of schedules with respect to a deadline. As such, scarcity consumes cognitive resources such as attention, working memory, and executive control and elicits a range of systematic and even counter-productive cognitive and behavioral responses as a result. Specifically, scarcity induces an attentional focus on the problem at hand, which facilitates performance by enhancing cognitive processing of information relevant to the problem, increasing the efficiency of resource use, and stabilizing the perception of value. Such prioritization of the problem at hand may seem advantageous, but it can produce undesirable consequences. For example, scarcity causes myopic and impulsive behavior, prioritizing short-term gains over long-term gains. Ironically, scarcity can also result in a failure to notice beneficial information in the environment that alleviates the condition of scarcity. More detrimentally, scarcity directly impairs cognitive function, which can lead to suboptimal decisions and choices that exacerbate the condition of scarcity. Thus, scarcity means not only a shortage of physical resources (e.g., money or time) but also a deficit of cognitive resources (e.g., attention, executive control). The cognitive deficits under scarcity are particularly problematic because they impair performance and lead to counter-productive behaviors that deepen the cycle of scarcity. In addition, people under financial scarcity suffer from stigmas and stereotypes associated with poverty. These social perceptions of poverty further burden the mind by consuming cognitive resources, weakening performance in the poor. Understanding the cognitive and behavioral responses to scarcity provides new insights into why the poor remain poor, identifying the psychological causes of scarcity, and illuminating potential interventions to stop the cycle of scarcity. These insights have important implications for the design and the implementation of policies and services targeting the populations under scarcity.
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Cerebral Palsy From a Developmental Psychology Perspective
Karen Lidzba
Cerebral palsy (CP) is defined as non-progressive damage to the brain at or around birth, which leads to varying symptoms depending on the extent and location of damage. The leading symptom is sensory-motor impairment of varying expression, but additional perceptual, cognitive, and socio-emotional symptoms are common. CP can be divided into four types, with bilateral spastic being by far the most frequent, followed by the unilateral spastic, the dyskinetic, and the ataxic variants. The intellectual, linguistic, and cognitive profile of CP is extremely variant, but all qualities correlate more or less with CP type and motor impairment. Early diagnosis is important since early intervention may promote all developmental dimensions. Generally, individuals with unilateral spastic CP have the best (almost normal) intellectual, linguistic, and cognitive outcomes, while those with bilateral spastic CP fare the worst. Language perception is often an individual strength, while language expression, and particularly speech, may be heavily impaired. Attention and executive functions are often impaired as compared to typically developing controls, even in those children with normal intellectual functioning. The same holds true for visual perceptual functions, which are impaired in almost half of all children and adolescents with CP. The potential neuropsychological dysfunctions are a risk factor for arithmetic functions and literacy. Obstacles to participate in society are high for individuals with CP and heavily dependent on their motor, language, intellectual, and cognitive functions. However, quality of life is good for most children and adolescents, and they develop a sound self-concept. On the other side, bully experience is more common than amongst typically developing children and is associated with behavior problems and executive dysfunction. The development of children and adolescents with CP is determined by a complex interplay between physical, intellectual, and neuropsychological functions.
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Attentional Processes in Sport and Performance
Aidan Moran and John Toner
We are constantly bombarded by information. Therefore, during every waking moment of our lives, we face decisions about which stimuli to prioritize and which ones to ignore. To complicate matters, the information that clamors for our attention includes not only events that occur in the world around us but also experiences that originate in the subjective domain of our own thoughts and feelings. The end result is that our minds can consciously attend to only a fraction of the rich kaleidoscope of information and experiences available to us from our senses, thoughts, memories, and imagination. Attentional processes such as “concentration,” or the ability to focus on the task at hand while ignoring distractions, are crucial for success in sport and other domains of skilled performance. To illustrate, Venus Williams, one of the greatest tennis players of all time, proclaimed that “for the players it is complete and pure focus. You don’t see anything or hear anything except the ball and what’s going on in your head.” For psychological scientists, concentration resembles a mental spotlight (like the head-mounted torch that miners and divers wear in dark environments) that illuminates targets located either in the external world around us or in the internal world of our subjective experiences. A major advantage of this spotlight metaphor is that it shows us that concentration is never “lost”—although it can be diverted to targets (whether in the external world or inside our heads) that are irrelevant to the task at hand. Research on attentional processes in sport and performance has been conducted in cognitive psychology (the study of how the mind works), cognitive sport psychology (the study of mental processes in athletes), and cognitive neuroscience (the study of how brain systems give rise to mental processes). From this research, advances have been made both in measuring attentional processes and in understanding their significance in sport and performance settings. For example, pupillometry, or the study of changes in pupil diameter as a function of cognitive processing, has been used as an objective index of attentional effort among skilled performers such as musicians and equestrian athletes. Next, research suggests that a heightened state of concentration (i.e., total absorption in the task at hand) is crucial to the genesis of “flow” states (i.e., rare and elusive moments when everything seems to come together for the performer) and optimal performance in athletes. More recently, studies have shown that brief mindfulness intervention programs, where people are trained to attend non-judgmentally to their own thoughts, feelings, and sensations, offer promise in the quest to enhance attentional skills in elite athletes. By contrast, anxiety has been shown to divert skilled performers’ attention to task-irrelevant information—sometimes triggering “choking” behavior or the sudden and significant deterioration of skilled performance. Finally, concentration strategies such as “trigger words” (i.e., the use of short, vivid, and positively phrased verbal reminders such as “this ball now”) are known to improve athletes’ ability to focus on a specific target or to execute skilled actions successfully.
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Deep Learning Networks and Visual Perception
Grace W. Lindsay and Thomas Serre
Deep learning is an approach to artificial intelligence (AI) centered on the training of deep artificial neural networks to perform complex tasks. Since the early 21st century, this approach has led to record-breaking advances in AI, allowing computers to solve complex board games, video games, natural language-processing tasks, and vision problems. Neuroscientists and psychologists have also utilized these networks as models of biological information processing to understand language, motor control, cognition, audition, and—most commonly—vision. Specifically, early feedforward network architectures were inspired by visual neuroscience and are used to model neural activity and human behavior. They also provide useful representations of the perceptual space of images. The extent to which these models match data, however, depends on the methods used to characterize and compare them. The limitations of these feedforward neural networks to account for, for example, simple visual reasoning tasks, suggests that feedback mechanisms may be necessary to solve visual recognition tasks beyond image categorization.
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Visual Search
Jeremy Wolfe
Visual search is the process of finding things that you are looking for in a world full of things that you are not looking for. Search tasks are ubiquitous. Many are so routine that we do not think of them as search tasks (e.g., Where is the space bar on the keyboard?). Others are more taxing (Where is the cat hiding?) and/or more important (Is there a tumor in this x-ray?). The need for search arises out of limits on the amount of visual input that can be fully processed at one time. Research in this area seeks to understand how observers find the object or objects of search as well as how, when, and why clearly visible targets can be missed by those observers. To understand how visual searches proceed, it is important to describe the forces that guide attention to different objects and locations in the field and to know what is being seen at locations away from the current focus of attention.
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Multistable Perception
Alexander Pastukhov
Multistable perception is produced by stimuli that are consistent with two or more different comparably likely perceptual interpretations. After the initial perception is resolved in favor of one of the interpretations, continued viewing leads to fluctuating subjective experience, as perception spontaneously switches between alternative states. Multistable perception occurs for different modalities, including visual, auditory, tactile, olfactory perception and proprioception, and various conflicting sensory representations, such as eye dominance, depth, motion, or meaning. Despite large differences, multistable stimuli produce quantitatively similar perceptual experience with stereotypical distribution of durations of dominance phases, similar dependence on the absolute and relative strength of competing perceptual interpretations, prior perceptual history, presentation method, attention, and volitional control, and so on. Taken together, this shows that multistable perception reflects the action of general canonical perceptual mechanisms whose purpose is to resolve the conflicting evidence and ensure a single dominant perception that can be used for action. Thus, it informs us about mechanisms of perceptual decision making, including the importance of feedback mechanisms in resolving perceptual ambiguity and the role of parietal and frontal regions in facilitating changes in perception. Multistable perception provides useful constraints for models, inspiring a plethora of models of perception that combine neurally plausible mechanisms, such as neural adaptation and inhibition, or are based on the idea of predictive coding. The sensitive nature of multistable perception makes a valuable experimental tool that can reveal even minor differences due to low- or high-level influences, including genetic or clinical cases. As such, it is an important tool in studying neural and behavioral correlates of consciousness as it dissociates perception from the stimulus.
