What is the time course of visual attention? Attentional blink studies have found that the 2nd of 2 targets is often missed when presented within about 500 ms from the 1st target, resulting in theories about relatively long-lasting capacity limitations or bottlenecks. Earlier studies, however, reported quite the opposite finding: Attention is transiently enhanced, rather than reduced, for several hundreds of milliseconds after a relevant event. The authors present a general theory, as well as a working computational model, that integrate these findings. There is no central role for capacity limitations or bottlenecks. Central is a rapidly responding gating system (or attentional filter) that seeks to enhance relevant and suppress irrelevant information. When items sufficiently match the target description, they elicit transient excitatory feedback activity (a "boost" function), meant to provide access to working memory. However, in the attentional blink task, the distractor after the target is accidentally boosted, resulting in subsequent strong inhibitory feedback response (a "bounce"), which, in effect, closes the gate to working memory. The theory explains many findings that are problematic for limited-capacity accounts, including a new experiment showing that the attentional blink can be postponed.

Decades of reading research have led to sophisticated accounts of single-word recognition and, in parallel, accounts of eye-movement control in text reading. Although these two endeavors have strongly advanced the field, their relative independence has precluded an integrated account of the reading process. To bridge the gap, we here present a computational model of reading, OB1-reader, which integrates insights from both literatures. Key features of OB1 are as follows: (1) parallel processing of multiple words, modulated by an attentional window of adaptable size; (2) coding of input through a layer of open bigram nodes that represent pairs of letters and their relative position; (3) activation of word representations based on constituent bigram activity, competition with other word representations and contextual predictability; (4) mapping of activated words onto a spatiotopic sentence-level representation to keep track of word order; and (5) saccade planning, with the saccade goal being dependent on the length and activation of surrounding word units, and the saccade onset being influenced by word recognition. A comparison of simulation results with experimental data shows that the model provides a fruitful and parsimonious theoretical framework for understanding reading behavior. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The question of how learners’ motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education. Here, we present our understanding of how influential theories of academic motivation have conceptualized reciprocal interactions between motivation and achievement and the kinds of evidence that support this reciprocity. While the reciprocal nature of the relationship between motivation and academic achievement has been established in the literature, further insights into several features of this relationship are still lacking. We therefore present a research agenda where we identify theoretical and methodological challenges that could inspire further understanding of the reciprocal relationship between motivation and achievement as well as inform future interventions. Specifically, the research agenda includes the recommendation that future research considers (1) multiple motivation constructs, (2) behavioral mediators, (3) a network approach, (4) alignment of intervals of measurement and the short vs. long time scales of motivation constructs, (5) designs that meet the criteria for making causal, reciprocal inferences, (6) appropriate statistical models, (7) alternatives to self-reports, (8) different ways of measuring achievement, and (9) generalizability of the reciprocal relations to various developmental, ethnic, and sociocultural groups.

Adaptive behavior in a changing world requires flexibly adapting one’s rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the ‘learning rate’). However, the brain mechanisms, and in particular the neuromodulators, involved in this process are still largely unknown. The brain-wide neurophysiological effects of the catecholamines norepinephrine and dopamine on stimulus-evoked cortical responses suggest that the catecholamine systems are well positioned to regulate learning about environmental change, but more direct evidence for a role of this system is scant. Here, we report evidence from a study employing pharmacology, scalp electrophysiology and computational modeling (N = 32) that suggests an important role for catecholamines in learning rate regulation. We found that the P3 component of the EEG—an electrophysiological index of outcome-evoked phasic catecholamine release in the cortex—predicted learning rate, and formally mediated the effect of prediction-error magnitude on learning rate. P3 amplitude also mediated the effects of two computational variables—capturing the unexpectedness of an outcome and the uncertainty of a preexisting belief—on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants’ baseline learning rate. Our findings provide converging evidence for a causal role of the human catecholamine systems in learning-rate regulation as a function of environmental change. Belief updating in response to changes in the environment is crucial for adaptive behavior. We examined the role of the human catecholamine (norepinephrine and dopamine) systems in this process, using a combination of pharmacology, scalp electrophysiology and computational modeling. We found that the P3 component of the event-related potential—an electrophysiological index of phasic catecholamine release in the cortex—predicted learning rate and mediated the effect of prediction-error magnitude on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants’ natural learning rate. These findings may reflect the catecholaminergic regulation of belief updating following environmental change.