Working memory (WM) has limited capacity. This leaves attention with the important role of allowing into storage only the most relevant information. It is increasingly evident that attention is ...equally crucial for prioritizing representations within WM as the importance of individual items changes. Retrospective prioritization has been proposed to result from a focus of internal attention highlighting one of several representations. Here, we suggest an updated model, in which prioritization acts in multiple steps: first orienting towards and selecting a memory, and then reconfiguring its representational state in the service of upcoming task demands. Reconfiguration sets up an optimized perception–action mapping, obviating the need for sustained attention. This view is consistent with recent literature, makes testable predictions, and links WM with task switching and action preparation.
While working memory (WM) allows us to store past information, its function is to guide future behavior. Given this role, the tight link between how WMs are maintained and how they are read out to be ...transformed into context-appropriate actions remains relatively unexplored. Beyond helping us understand memory-guided behavior, focusing on WM readout may also help us better understand the neural basis of memory maintenance.
Perceptual decisions occur after the evaluation and integration of momentary sensory inputs, and dividing attention between spatially disparate sources of information impairs decision performance. ...However, it remains unknown whether dividing attention degrades the precision of sensory signals, precludes their conversion into decision signals, or dampens the integration of decision information toward an appropriate response. Here we recorded human electroencephalographic (EEG) activity while participants categorized one of two simultaneous and independent streams of visual gratings according to their average tilt. By analyzing trial-by-trial correlations between EEG activity and the information offered by each sample, we obtained converging behavioral and neural evidence that dividing attention between left and right visual fields does not dampen the encoding of sensory or decision information. Under divided attention, momentary decision information from both visual streams was encoded in slow parietal signals without interference but was lost downstream during their integration as reflected in motor mu- and beta-band (10-30 Hz) signals, resulting in a "leaky" accumulation process that conferred greater behavioral influence to more recent samples. By contrast, sensory inputs that were explicitly cued as irrelevant were not converted into decision signals. These findings reveal that a late cognitive bottleneck on information integration limits decision performance under divided attention, and places new capacity constraints on decision-theoretic models of information integration under cognitive load.
Cognitive flexibility is critical for intelligent behavior. However, its execution is effortful and often suboptimal. Recent work indicates that flexible behavior can be improved by the prospect of ...reward, which suggests that rewards optimize flexible control processes. Here we investigated how different reward prospects influence neural encoding of task rule information to optimize cognitive flexibility. We applied representational similarity analysis to human electroencephalograms, recorded while female and male participants performed a rule-guided decision-making task. During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high-reward trials. Critically, high reward boosted neural coding of the active task rule, and the extent of this increase was associated with improvements in task performance. Additionally, the effect of high reward on task rule coding was most pronounced on switch trials, where rules were updated relative to the previous trial. These results suggest that reward prospect can promote cognitive performance by strengthening neural coding of task rule information, helping to improve cognitive flexibility during complex behavior.
The importance of motivation is evident in the ubiquity with which reward prospect guides adaptive behavior and the striking number of neurological conditions associated with motivational impairments. In this study, we investigated how dynamic changes in motivation, as manipulated through reward, shape neural coding for task rules during a flexible decision-making task. The results of this work suggest that motivation to obtain reward modulates the encoding of task rules needed for flexible behavior. The extent to which reward increased task rule coding also tracked improvements in behavioral performance under high-reward conditions. These findings help to inform how motivation shapes neural processing in the healthy human brain.
Despite increased awareness of the lack of gender equity in academia and a growing number of initiatives to address issues of diversity, change is slow, and inequalities remain. A major source of ...inequity is gender bias, which has a substantial negative impact on the careers, work-life balance, and mental health of underrepresented groups in science. Here, we argue that gender bias is not a single problem but manifests as a collection of distinct issues that impact researchers' lives. We disentangle these facets and propose concrete solutions that can be adopted by individuals, academic institutions, and society.
Temporal orienting improves sensory processing, akin to other top-down biases. However, it is unknown whether these improvements reflect increased neural gain to any stimuli presented at expected ...time points, or specific tuning to task-relevant stimulus aspects. Furthermore, while other top-down biases are selective, the extent of trade-offs across time is less well characterized. Here, we tested whether gain and/or tuning of auditory frequency processing in humans is modulated by rhythmic temporal expectations, and whether these modulations are specific to time points relevant for task performance. Healthy participants (
= 23) of either sex performed an auditory discrimination task while their brain activity was measured using magnetoencephalography/electroencephalography (M/EEG). Acoustic stimulation consisted of sequences of brief distractors interspersed with targets, presented in a rhythmic or jittered way. Target rhythmicity not only improved behavioral discrimination accuracy and M/EEG-based decoding of targets, but also of irrelevant distractors preceding these targets. To explain this finding in terms of increased sensitivity and/or sharpened tuning to auditory frequency, we estimated tuning curves based on M/EEG decoding results, with separate parameters describing gain and sharpness. The effect of rhythmic expectation on distractor decoding was linked to gain increase only, suggesting increased neural sensitivity to any stimuli presented at relevant time points.
Being able to predict when an event may happen can improve perception and action related to this event, likely due to the alignment of neural activity to the temporal structure of stimulus streams. However, it is unclear whether rhythmic increases in neural sensitivity are specific to task-relevant targets, and whether they competitively impair stimulus processing at unexpected time points. By combining magnetoencephalography and encephalographic recordings, neural decoding of auditory stimulus features, and modeling, we found that rhythmic expectation improved neural decoding of both relevant targets and irrelevant distractors presented and expected time points, but did not competitively impair stimulus processing at unexpected time points. Using a quantitative model, these results were linked to nonspecific neural gain increases due to rhythmic expectation.
It is often assumed that information in visual working memory (vWM) is maintained via persistent activity. However, recent evidence indicates that information in vWM could be maintained in an ...effectively "activity-silent" neural state. Silent vWM is consistent with recent cognitive and neural models, but poses an important experimental problem: how can we study these silent states using conventional measures of brain activity? We propose a novel approach that is analogous to echolocation: using a high-contrast visual stimulus, it may be possible to drive brain activity during vWM maintenance and measure the vWM-dependent impulse response. We recorded electroencephalography (EEG) while participants performed a vWM task in which a randomly oriented grating was remembered. Crucially, a high-contrast, task-irrelevant stimulus was shown in the maintenance period in half of the trials. The electrophysiological response from posterior channels was used to decode the orientations of the gratings. While orientations could be decoded during and shortly after stimulus presentation, decoding accuracy dropped back close to baseline in the delay. However, the visual evoked response from the task-irrelevant stimulus resulted in a clear re-emergence in decodability. This result provides important proof-of-concept for a promising and relatively simple approach to decode "activity-silent" vWM content using non-invasive EEG.
Extensive research has examined how information is maintained in working memory (WM), but it remains unknown how WM is used to guide behavior. We addressed this question by combining human ...electrophysiology (50 subjects, male and female) with pattern analyses, cognitive modeling, and a task requiring the prolonged maintenance of two WM items and priority shifts between them. This enabled us to discern neural states coding for memories that were selected to guide the next decision from states coding for concurrently held memories that were maintained for later use, and to examine how these states contribute to WM-based decisions. Selected memories were encoded in a functionally active state. This state was reflected in spontaneous brain activity during the delay period, closely tracked moment-to-moment fluctuations in the quality of evidence integration, and also predicted when memories would interfere with each other. In contrast, concurrently held memories were encoded in a functionally latent state. This state was reflected only in stimulus-evoked brain activity, tracked memory precision at longer timescales, but did not engage with ongoing decision dynamics. Intriguingly, the two functional states were highly flexible, as priority could be dynamically shifted back and forth between memories without degrading their precision. These results delineate a hierarchy of functional states, whereby latent memories supporting general maintenance are transformed into active decision circuits to guide flexible behavior.
Working memory enables maintenance of information that is no longer available in the environment. Abundant neuroscientific work has examined where in the brain working memories are stored, but it remains unknown how they are represented and used to guide behavior. Our study shows that working memories are represented in qualitatively different formats, depending on behavioral priorities. Memories that are selected for guiding behavior are encoded in an active state that transforms sensory input into decision variables, whereas other concurrently held memories are encoded in a latent state that supports precise maintenance without affecting ongoing cognition. These results dissociate mechanisms supporting memory storage and usage, and open the door to reveal not only where memories are stored but also how.
Value-based decision-making is often studied in a static context, where participants decide which option to select from those currently available. However, everyday life often involves an additional ...dimension: deciding when to select to maximize reward. Recent evidence suggests that agents track the latent reward of an option, updating changes in their latent reward estimate, to achieve appropriate selection timing (latent reward tracking). However, this strategy can be difficult to distinguish from one in which the optimal selection time is estimated in advance, allowing an agent to wait a predetermined amount of time before selecting, without needing to monitor an option's latent reward (distance-to-goal tracking). Here, we show that these strategies can in principle be dissociated. Human brain activity was recorded using electroencephalography (EEG), while female and male participants performed a novel decision task. Participants were shown an option and decided when to select it, as its latent reward changed from trial-to-trial. While the latent reward was uncued, it could be estimated using cued information about the option's starting value and value growth rate. We then used representational similarity analysis (RSA) to assess whether EEG signals more closely resembled latent reward tracking or distance-to-goal tracking. This approach successfully dissociated the strategies in this task. Starting value and growth rate were translated into a distance-to-goal signal, far in advance of selecting the option. Latent reward could not be independently decoded. These results demonstrate the feasibility of using high temporal resolution neural recordings to identify internally computed decision variables in the human brain.
Reward-seeking behavior involves acting at the right time. However, the external world does not always tell us when an action is most rewarding, necessitating internal representations that guide action timing. Specifying this internal neural representation is challenging because it might stem from a variety of strategies, many of which make similar predictions about brain activity. This study used a novel approach to test whether alternative strategies could be dissociated in principle. Using representational similarity analysis (RSA), we were able to distinguish between candidate internal representations for selection timing. This shows how pattern analysis methods can be used to measure latent decision information in noninvasive neural data.
Working memory (WM) is important for guiding behaviour, but not always for the next possible action. Here we define a WM item that is currently relevant for guiding behaviour as the functionally ..."active" item; whereas items maintained in WM, but not immediately relevant to behaviour, are defined as functionally "latent". Traditional neurophysiological theories of WM proposed that content is maintained via persistent neural activity (e.g., stable attractors); however, more recent theories have highlighted the potential role for "activity-silent" mechanisms (e.g., short-term synaptic plasticity). Given these somewhat parallel dichotomies, functionally active and latent cognitive states of WM have been associated with storage based on persistent-activity and activity-silent neural mechanisms, respectively. However, in this article we caution against a one-to-one correspondence between functional and activity states. We argue that the principal theoretical requirement for active and latent WM is that the corresponding neural states play qualitatively different functional roles. We consider a number of candidate solutions, and conclude that the neurophysiological mechanisms for functionally active and latent WM items are theoretically independent of the distinction between persistent activity-based and activity-silent forms of WM storage.
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