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Memorability denotes a stimulus-intrinsic property that results in stimuli being more likely to be remembered or forgotten. The effect is consistent across observers and can be measured in various stimuli such as faces and scene images. Long-term memory (LTM) paradigms have been used to measure memorability with studies demonstrating long-term memorability effects via incidental and intentional encoding mechanisms. The current study expands the understanding of memorability and examines whether memorability modulates the ability to unexpectedly report an image at short intervals. Three sets of experiments (using faces and scenes) with replications used an Attribute Amnesia (AA) paradigm to measure the effect of memorability on incidental visual working memory (VWM) processes while controlling attentional priorities. When participants had to identify the target image in the immediate trial unexpectedly, we observed that memorable targets yielded a higher accuracy compared to forgettable targets, suggesting that people incidentally remember more information about memorable stimuli even across very short intervals. This memorability benefit was observed for both stimulus types but was greater for faces compared to scenes. Thus, memorability modulates the ability to remember stimuli in an incidental VWM task. These results expand our understanding of the mechanisms behind memorability effects, and how attentional filters impact memory traces.

Prior research has shown that visual working memory capacity is enhanced for meaningful stimuli (i.e., real-world objects) compared to abstract shapes (i.e., colored circles). Furthermore, a simple feature that is part of a real-world object is better remembered than the same feature presented on an unrecognizable shape, suggesting that meaningful objects can serve as an effective scaffold in memory. Here, we hypothesized that the shape of meaningful objects would be better remembered incidentally than the shape of non-meaningful objects in a color memory task where identity itself is task-irrelevant. We used a surprise-trial paradigm in which participants performed a color memory task for several trials before being probed with a surprise trial that asked them about the shape of the last object they saw. Across three experiments, we found a memory advantage for recognizable shapes relative to scrambled and unrecognizable versions of these shapes (Exp. 1) that was robust across different encoding times (Exp. 2), and the addition of a verbal suppression task (Exp. 3). In contrast, when we asked about the location of objects in a surprise trial, we did not observe any difference between the two stimulus types (Exp. 4). These results show that identifying information about a meaningful object is encoded into working memory despite being task-irrelevant. This privilege for meaningful shape information does not exhibit a trade-off with location memory, suggesting that meaningful identity influences representations of visual working memory in higher-level visual regions without altering the use of spatial reference frames at the lower level.

It is well established that search tasks within natural scenes induce the encoding of information about the scene and the target, particularly when the scene is complex or repeated. In this study, we asked whether searches always necessitate memory encoding by using a simple search task to minimize incentives for incidental encoding followed immediately by a visual recall task. Participants repeatedly searched for and located an easily-detected item in novel scenes for numerous trials before being unexpectedly prompted to draw the entire scene (Experiment 1) or the object that they had been locating (Experiment 2) directly after being shown a search image. The similarity of the drawings to the original information was assessed by naïve raters. Surprise- trial drawings of the scene and search target were both poorly recognizable as representations of the scene / target itself. The same drawers produced highly recognizable scenes and objects on the next trial when they had an expectation to draw the image. Experiment 3 further showed that the poor surprise trial memory could not merely be attributed to interference caused by the unexpected drawing prompt. Our findings replicate Attribute Amnesia (Chen & Wyble, 2015) using the method of visual recall through drawing: it is possible to locate a target without creating a memory of it or the scene that it was in, even if attended to just a few seconds prior. This disconnection between attention and memory might reflect a fundamental property of cognitive computations designed to optimize task performance and minimize resource use.

A key problem that the mind has to solve is gating which information will be stored in memory. The conventional idea of sensory attention as a working memory (WM) selection mechanism is widely accepted. The present studies challenged this traditional view by showing that even fully attended information may not be encoded into WM, indicating a WM reselection process for attended information. More importantly, the mechanism of WM reselection was explored and the surprising finding was that the reselection operates through a dimensional-memory-filter. That is, the brain could selectively encode the attended information from one feature dimension while blocking others from another dimension at the same time. However, once a feature is selected, all concurrently attended information from the same dimension would be automatically encoded into WM regardless of whether they are necessary for the task. These findings have critical implications for understanding neural mechanisms of memory formation.

As our environment is frequently changing, it is common that our expectations are violated by unexpected stimuli or events, which leaves us uncertain about which pieces of information will be useful in the future. It is unclear how an expectation violation affects the subsequent control settings for processing of information. The current study directly addressed this issue by employing a double-surprise-trial paradigm based on the attribute amnesia task (Chen & Wyble, 2015a). In Experiment 1, participants were asked to report the location of a target letter presented among distractor digits on several trials and were then unexpectedly asked to report a different attribute (color or identity) of the target letter. In the next trial, participants were asked another unexpected question about the other attribute (identity or color respectively). The results show that, despite participants' poor performance in the first surprise trial, which replicated the attribute amnesia effect, their memory performance in the second surprise trial was dramatically improved, even when the probed attribute was different from that of the first surprise trial. This was also true in Experiment 2, where 15 trials were inserted between the two surprise trials. Experiment 3 further clarified that this effect is not triggered by the mere presence of a surprise test, but rather the violation of expectation about the nature of a surprise test. These results suggest the operation of an adaptive control mechanism that reduces the selectivity of processing in the face of unexpected events. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Attribute amnesia (AA) is a phenomenon in which participants have difficulty answering an unexpected question about an attended attribute of the most recent target stimulus. A similar situation can occur in cases of real-life eyewitness identification when the eyewitness did not explicitly try to remember the alleged perpetrator's face despite having attended to it. We found that AA is generalizable to novel faces, such that when participants were unexpectedly asked to identify a face, performance was poor, even though they had just attended to that face seconds ago (N = 40 each in an initial experiment and its replication). This finding shows that unexpected face identification is inaccurate even when the face had just been attended to and suffered minimal decay and interference, implying that AA can explain some cases of failure of eyewitness identification that cannot be attributed to a lack of attention or post-event interference.

Neuromatch Academy (NMA) designed and ran a fully online 3-week Computational Neuroscience Summer School for 1757 students with 191 teaching assistants (TAs) working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility.

Previous evidence demonstrated that individuals can recall a target's location in a search display even if location information is completely task-irrelevant. This finding raises the question: does this ability to automatically encode a single item's location into a reportable memory trace extend to other aspects of spatial information as well? We tested this question using a paradigm designed to elicit attribute amnesia (Chen & Wyble, Psychological Science, 26(2) 203-210, 2015a). Participants were initially asked to report the location of a target letter among digits with stimuli arranged to form one of two or four spatial configurations varying randomly across trials. After completing numerous trials that matched their expectations, participants were surprised with a series of unexpected questions probing their memory for various aspects of the display they had just viewed. Participants had a profound inability to report which spatial configuration they had just perceived when the target's location was not unique to a specific configuration (i.e., orthogonal). Despite being unable to report the most recent configuration, answer choices on the surprise trial were focused around previously seen configurations, rather than novel configurations. Thus, there were clear memories of the set of configurations that had been viewed during the experiment but not of the specific configuration from the most recent trial. This finding helps to set boundary conditions on previous findings regarding the automatic encoding of location information into memory.

We investigated the extents of automaticity in location and orientation encoding in visual working memory (VWM) by manipulating their task relevance and assessing the amount of resource recruited by their encoding. Across five experiments, participants were surprised with a location report trial (Experiment 1A, 2A, and 3) or an orientation report trial (Experiment 2A and 2B) at a point when only the item's color had been task-relevant. This was followed by control trials to assess the memory quality of color when location or orientation had become task-relevant. We found the surprise trial performance to be significantly worse than the first control trial for both location and orientation, although to a greater extent for orientation for which there was virtually no measurable information from the subjects' reports. This was the case even when encoding was the only incidental memory process before the control trials (Experiment 2A and 2B), and the surprise memory costs cannot be attributed to the unexpectedness inherent to the surprise question (Experiment 3). The control trials revealed a consistent reduction of color memory only in the orientation experiments. These results suggest that although location encoding is more automatic than orientation, neither is encoded in a fully automatic manner. Our results show that incidentally encoded location is only coarse-grained, constraining the spatial precision of space-based indexing systems. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

There have been conflicting findings on the degree to which rapidly deployed visual attention is selective for depth, and this issue has important implications for attention models. Previous findings have attempted to find depth-based cueing effects on such attention using reaction time (RT) measures for stimuli presented in stereo goggles with a display screen. Results stemming from such approaches have been mixed, depending on whether target/distractor discrimination was required. To help clarify the existence of such depth effects, we have developed a paradigm that measures accuracy rather than RT in an immersive virtual-reality environment, providing a more appropriate context of depth. Three modified Posner Cueing paradigms were run to test for depth-specific rapid attentional selectivity. Participants fixated a cross while attempting to identify a rapidly masked black letter preceded by a red cue that could be valid in depth, side, or both. In Experiment 1a, a potent cueing effect was found for lateral cueing validity, but a weak effect was found for depth despite an extreme difference in virtual depth (1 vs. 300 m). In Experiment 1b, a near-replication of 1a, the lateral effect replicated while the depth effect did not. Finally, in Experiment 2, to increase the depth cue's effectiveness, the letter matched the cue's color, and the presentation duration was increased; however, again only a minimal depth-based cueing effect - no greater than that of Experiment 1a - was observed. Thus, we conclude that rapidly deployed attention is driven largely by spatiotopic rather than depth-based information.

A quintessential challenge for any perceptual system is the need to focus on task-relevant information without being blindsided by unexpected, yet important information. The human visual system incorporates several solutions to this challenge, 1 of which is a reflexive covert attention system that is rapidly responsive to both the physical salience and the task-relevance of new information. This article presents a model that simulates behavioral and neural correlates of reflexive attention as the product of brief neural attractor states that are formed across the visual hierarchy when attention is engaged. Such attractors emerge from an attentional gradient distributed over a population of topographically organized neurons and serve to focus processing at 1 or more locations in the visual field, while inhibiting the processing of lower priority information. The model moves toward a resolution of key debates about the nature of reflexive attention, such as whether it is parallel or serial, and whether suppression effects are distributed in a spatial surround, or selectively at the location of distractors. The model also develops a framework for understanding the neural mechanisms of visual attention as a spatiotopic decision process within a hierarchy and links them to observable correlates such as accuracy, reaction time (RT), and the N2pc and PD components of the electroencephalogram (EEG). This last contribution is the most crucial for repairing the disconnect that exists between our understanding of behavioral and neural correlates of attention. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Visual search is greatly affected by the appearance rate of given target types, such that low prevalence items are harder to detect, which has consequences for real-world search tasks where target frequency cannot be balanced. However, targets that are highly-representative of a categorically-defined task set are also easier to find. We hypothesized that targets which are highly-representative are less vulnerable to low prevalence effects because an observer’s attentional set prioritizes guidance toward them even when they are rare. We assessed this hypothesis by first determining the categorical structure of “prohibited carry-ons,” via an exemplar naming task and used this structure to assess how category representativeness interacted with prevalence. Specifically, from the exemplar naming task we selected a commonly named (knives) and rarely named (gas cans) target for a search task in which one of the targets was shown infrequently. As predicted, highly-representative targets were found more easily than their less representative counterparts, but they also were less affected by prevalence manipulations. Experiment 1b replicated the results with targets matched for emotional valence (water bottles and fireworks). These findings demonstrate the powerful explanatory power of theories of attentional guidance that incorporate the dynamic influence of recent experience with the knowledge that comes from life experience to better predict behavioral outcomes associated with high-stakes search environments.

Working memory allows us to hold specific pieces of information in an active and easily retrieved state, but what happens to that information during an unexpected interruption between study and test? To answer this question, we used a surprise trial paradigm in which an unexpected event precedes a probe of the observer’s memory for a search target. In the first set of experiments, participants were tasked to report the identity of the target letter before unexpectedly being asked to read a task-irrelevant passage. We observed that the introduction of this passage interfered with the observer’s memory of the target letter, but this interference only occurred after participants had experience completing the task without interruption. However, a remember cue placed just prior to the reading prompt reduced this cost, suggesting that participants can rapidly reinforce information about the target in working memory to resist the interference. We then used this same cueing manipulation to test whether information in an attribute amnesia paradigm, which unexpectedly probes an attribute relevant to target selection but irrelevant to participant’s response expectations, could also be protected against unexpected interference. Using this paradigm, we observed that a remember cue did not improve performance following the surprising event, which supports theories that attribute amnesia is not caused solely by interference. These results reveal both the vulnerability and flexibility of working memory, and demonstrate the importance of understanding how task experience establishes expectations that impact the underlying cognitive representations formed by the observer.

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.

The Learning Salon is an online weekly forum for discussing points of contention and common ground in biological and artificial learning. Hosting neuroscientists, computer scientists, AI researchers, and philosophers, the Salon promotes short talks and long discussions, committed to an ethos of participation, horizontality, and inclusion.

Previous research has shown that Attentional blink (AB) data can differ between tasks, or subjects and it can be challenging to interpret these differences. In this paper, we provided a ready-to-use tool that allows researchers to map their data onto the episodic Simultaneous Type, Serial Token (eSTST) model. This tool uses the Markov Chain Monte Carlo algorithm to find the best set of 3 model parameters to simulate a given AB pattern. These 3 parameters have cognitive interpretations, such that differences in these parameters between different paradigms can be used for inferences about the timing of attentional deployment or the encoding of memory. Additionally, our tool allows for a combination of quantitative fitting against the overall pattern of data points, and qualitative fitting for theoretically important features. We demonstrate the algorithm using several data sets, showing that it can find cognitively interpretable parameter sets for some of them, but fails to find a good fit for one data set. This indicates an explanatory boundary of the eSTST model. Finally, we provide a feature to avoid overfitting of individual data points with high uncertainty, such as in the case of individual participant data.

Attribute amnesia (AA) is a phenomenon in which participants have difficulty answering an unexpected question about an attended attribute of the most recent target stimulus. A similar situation can occur in cases of real-life eyewitness identification when the eyewitness did not explicitly try to remember the alleged perpetrator's face despite having attended to it. We found that AA is generalizable to novel faces, such that when participants were unexpectedly asked to identify a face, performance was poor, even though they had just attended to that face seconds ago (N = 40 each in an initial experiment and its replication). This finding shows that unexpected face identification is inaccurate even when the face had just been attended to and suffered minimal decay and interference, implying that AA can explain some cases of failure of eyewitness identification that cannot be attributed to a lack of attention or post-event interference.

There have been conflicting findings on the degree to which rapidly deployed visual attention is selective for depth, and this issue has important implications for attention models. Previous findings have attempted to find depth-based cueing effects on such attention using reaction time measures for stimuli presented in stereo goggles with a display screen. Results stemming from such approaches have been mixed, depending on whether target/distractor discrimination was required. To help clarify the existence of such depth effects, we have developed a paradigm that measures accuracy rather than reaction time in an immersive virtual-reality environment, providing a more appropriate context of depth. Three modified Posner Cueing paradigms were run to test for depth-specific rapid attentional selectivity. Participants fixated a cross while attempting to identify a rapidly masked black letter preceded by a red cue that could be valid in depth, side, or both. In Experiment 1a, a potent cueing effect was found for lateral cueing validity, but a weak effect was found for depth despite an extreme difference in virtual depth (1 vs 300 meters). In Experiment 1b, a near-replication of 1a, the lateral effect replicated while the depth effect did not. Finally, in Experiment 2, to increase the depth cue’s effectiveness, the letter matched the cue’s color, and the presentation duration was increased; however, again only a minimal depth-based cueing effect – no greater than that of Experiment 1a – was observed. Thus, we conclude that rapidly deployed attention is driven largely by spatiotopic rather than depth-based information.

We investigated the extents of automaticity in location and orientation encoding in visual working memory (VWM) by manipulating their task relevance and assessing the amount of resource recruited by their encoding. Across five experiments, participants were surprised with a location report trial (Experiment 1A, 2A, and 3) or an orientation report trial (Experiment 2A and 2B) at a point when only the item’s color had been task relevant. This was followed by control trials to assess the memory quality of color when location or orientation had become task relevant. We found the surprise trial performance to be significantly worse than the first control trial for both location and orientation, although to a greater extent for orientation for which there was virtually no measurable information from the subjects’ reports. This was the case even when encoding was the only incidental memory process before the control trials (Experiment 2A and 2B), and the surprise memory costs cannot be attributed to the unexpectedness inherent to the surprise question (Experiment 3). The control trials revealed a consistent reduction of color memory only in the orientation experiments. These results suggest that although location encoding is more automatic than orientation, neither is encoded in a fully automatic manner. Our results show that incidentally encoded location is only coarse-grained, constraining the spatial precision of space-based indexing systems.

Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.

Attribute amnesia (AA) is a phenomenon in which participants are unable to answer an unexpected question about an attended attribute of the most recent target stimulus. Similarly, eyewitness identification is a real-life example of a memory test that is unexpected at the time of seeing an alleged perpetrator. We are thus interested in whether AA is generalizable to novel face identification, a finding that would help us understand the source of memory failures in eyewitness identification and a variety of real-world situations. We found that when participants were unexpectedly asked to identify a face, performance was poor, even though they had just attended to that face seconds ago. This finding shows that unexpected face identification is inaccurate even when the face had just been attended to and suffered minimal decay and interference, implying that some failures of eyewitness identification were already inevitable just after the crime had been witnessed.

In this critical review, we examine the current application of predictive models, e.g. classifiers, trained using ML to assist in interpretation of functional neuroimaging data. Our primary goal is to summarize how ML is being applied and critically assess the most common practices. Our review covers 250 studies published using ML and resting-state functional MRI (fMRI) to infer various dimensions of the human functional connectome. Results for lockbox performance was, on average, ~13% less accurate than performance measured through cross validation alone highlighting the importance of hold-out (“lockbox”) data which was included in only 16% of the studies surveyed. There was also a concerning lack of transparency across the key steps in training and evaluating predictive models. The summary of this literature underscores the importance of the use of a lockbox and highlights several methodological pitfalls that can be addressed by the imaging community. We argue that, ideally, studies are motivated both by the reproducibility and generalizability of findings as well as the potential clinical significance of the insights. We offer recommendations for principled integration of machine learning into the clinical neurosciences with the goal of advancing imaging biomarkers of brain disorders, understanding causative determinants for health risks, and parsing heterogeneous patient outcomes.

Visual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memory (WM) representations are built from the latent representations of visual knowledge and can then be reconstructed. The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items attributes to tokenized representations. The simulation results revealed that shape information for stimuli that the model was trained on, can be encoded and retrieved efficiently from latents in higher levels of the visual hierarchy. On the other hand, novel patterns that are completely outside the training set can be stored from a single exposure using only latents from early layers of the visual system. Moreover, the representation of a given stimulus can have multiple codes, representing specific visual features such as shape or color, in addition to categorical information. Finally, we validated our model by testing a series of predictions against behavioral results acquired from WM tasks. The model provides a compelling demonstration of visual knowledge yielding the formation of compact visual representation for efficient memory encoding.

Legacy conferences are costly and time consuming, and exclude scientists lacking various resources or abilities. During the 2020 pandemic, we created an online conference platform, Neuromatch Conferences (NMC), aimed at developing technological and cultural changes to make conferences more democratic, scalable, and accessible. We discuss the lessons we learned.

Machine learning has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With these powerful techniques comes the danger of overfitting of hyperparameters which can render results invalid. We refer to this problem as ‘overhyping’ and show that it is pernicious despite commonly used precautions. Overhyping occurs when analysis decisions are made after observing analysis outcomes and can produce results that are partially or even completely spurious. It is commonly assumed that cross-validation is an effective protection against overfitting or overhyping, but this is not actually true. In this article, we show that spurious results can be obtained on random data by modifying hyperparameters in seemingly innocuous ways, despite the use of cross-validation. We recommend a number of techniques for limiting overhyping, such as lock boxes, blind analyses, pre-registrations, and nested cross-validation. These techniques, are common in other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques in the neurosciences.

Scientific conferences and meetings have an important role in research, but they also suffer from a number of disadvantages: in particular, they can have a massive carbon footprint, they are time-consuming, and the high costs involved in attending can exclude many potential participants. The COVID-19 pandemic has led to the cancellation of many conferences, forcing the scientific community to explore online alternatives. Here, we report on our experiences of organizing an online neuroscience conference, neuromatch, that attracted some 3000 participants and featured two days of talks, debates, panel discussions, and one-on-one meetings facilitated by a matching algorithm. By offering most of the benefits of traditional conferences, several clear advantages, and with fewer of the downsides, we feel that online conferences have the potential to replace many legacy conferences.

The ability to select targets from an ongoing stream of visual information is critical to the successful management of visual attention. The Attentional Blink (AB), a phenomenon elicited using Rapid Serial Visual Presentation paradigms, allows for the assessment of the limits of the temporal visual system, and is reflected in a decrease in accuracy in the detection of the second of two targets when it occurs within 200–500ms of a first target. Evidence regarding the development of the AB is mixed and appears dependent on the task demands. Here we present data examining the AB across middle childhood, early adolescence, and adulthood using a color binding task in which participants had to detect, and report, the identity of two purple letters presented in a stream of black letters at a rate of 135ms/item. Results suggest that on these feature binding tasks, the depth of the AB is invariant across development, but that AB recovery gets faster with age. Further, the error data suggests important developments in temporal binding that are reflected both in a decrease in the number of swaps (where participants reverse the order of the targets but identify them correctly) and in the spread of temporal binding errors with age. These findings suggest that the characteristics of the AB and its development are task dependent, and also suggest that the development of binding abilities in visual search tasks mirror the time course of multisensory binding effects, perhaps suggesting a common mechanism.