Hierarchical categorization learning is associated with representational changes in the dorsal striatum and posterior frontal and parietal cortex.

Learning and recognition can be improved by sorting novel items into categories and subcategories. Such hierarchical categorization is easy when it can be performed according to learned rules (e.g., "if car, then automatic or stick shift" or "if boat, then motor or sail"). Here, we present results showing that human participants acquire categorization rules for new visual hierarchies rapidly, and that, as they do, corresponding hierarchical representations of the categorized stimuli emerge in patterns of neural activation in the dorsal striatum and in posterior frontal and parietal cortex. Participants learned to categorize novel visual objects into a hierarchy with superordinate and subordinate levels based on the objects' shape features, without having been told the categorization rules for doing so. On each trial, participants were asked to report the category and subcategory of the object, after which they received feedback about the correctness of their categorization responses. Participants trained over the course of a one-hour-long session while their brain activation was measured using functional magnetic resonance imaging. Over the course of training, significant hierarchy learning took place as participants discovered the nested categorization rules, as evidenced by the occurrence of a learning trial, after which performance suddenly increased. This learning was associated with increased representational strength of the newly acquired hierarchical rules in a corticostriatal network including the posterior frontal and parietal cortex and the dorsal striatum. We also found evidence suggesting that reinforcement learning in the dorsal striatum contributed to hierarchical rule learning.

The Architecture of Object-Based Attention.

The allocation of attention to objects raises several intriguing questions: What are objects, how does attention access them, what anatomical regions are involved? Here, we review recent progress in the field to determine the mechanisms underlying object-based attention. First, findings from unconscious priming and cueing suggest that the preattentive targets of object-based attention can be fully developed object representations that have reached the level of identity. Next, the control of object-based attention appears to come from ventral visual areas specialized in object analysis that project downward to early visual areas. How feedback from object areas can accurately target the object's specific locations and features is unknown but recent work in autoencoding has made this plausible. Finally, we suggest that the three classic modes of attention may not be as independent as is commonly considered, and instead could all rely on object-based attention. Specifically, studies show that attention can be allocated to the separated members of a group-without affecting the space between them-matching the defining property of feature-based attention. At the same time, object-based attention directed to a single small item has the properties of space-based attention. We outline the architecture of object-based attention, the novel predictions it brings, and discuss how it works in parallel with other attention pathways.

DeepAction: a MATLAB toolbox for automated classification of animal behavior in video.

The identification of animal behavior in video is a critical but time-consuming task in many areas of research. Here, we introduce DeepAction, a deep learning-based toolbox for automatically annotating animal behavior in video. Our approach uses features extracted from raw video frames by a pretrained convolutional neural network to train a recurrent neural network classifier. We evaluate the classifier on two benchmark rodent datasets and one octopus dataset. We show that it achieves high accuracy, requires little training data, and surpasses both human agreement and most comparable existing methods. We also create a confidence score for classifier output, and show that our method provides an accurate estimate of classifier performance and reduces the time required by human annotators to review and correct automatically-produced annotations. We release our system and accompanying annotation interface as an open-source MATLAB toolbox.

Exploring the frame effect.

Probes flashed within a moving frame are dramatically displaced (Özkan, Anstis, 't Hart, Wexler, & Cavanagh, 2021; Wong & Mack, 1981). The effect is much larger than that seen on static or moving probes (induced motion, Duncker, 1929; Wallach, Bacon, & Schulman, 1978). These flashed probes are often perceived with the separation they have in frame coordinates-a 100% effect (Özkan et al., 2021). Here, we explore this frame effect on flashed tests with several versions of the standard stimulus. We find that the frame effect holds for smoothly or abruptly displacing frames, even when the frame changed shape or orientation between the end points of its travel. The path could be nonlinear, even circular. The effect was driven by perceived not physical motion. When there were competing overlapping frames, the effect was determined by which frame was attended. There were a number of constraints that limited the effect. A static anchor near the flashes suppressed the effect but an extended static texture did not. If the probes were continuous rather than flashed, the effect was abolished. The observational reports of 30 online participants suggest that the frame effect is robust to many variations in its shape and path and leads to a perception of flashed tests in their locations relative to the frame as if the frame were stationary. Our results highlight the role of frame continuity and of the grouping of the flashes with the frame in generating the frame effect.

Caught in the ACTS: Defining Abstract Cognitive Task Sequences as an Independent Process.

Cognitive neuroscience currently conflates the study of serial responses (e.g., delay match to sample/nonsample, n-back) with the study of sequential operations. In this essay, our goal is to define and disentangle the latter, termed abstract cognitive task sequences (ACTS). Existing literatures address tasks requiring serial events, including procedural learning of implicit motor responses, statistical learning of predictive relationships, and judgments of attributes. These findings do not describe the behavior and underlying mechanism required to succeed at remembering to evaluate color, then shape; or to multiply, then add. A new literature is needed to characterize these sorts of second-order cognitive demands of studying a sequence of operations. Our second goal is to characterize gaps in knowledge related to ACTS that merit further investigation. In the following sections, we define more precisely what we mean by ACTS and suggest research questions that further investigation would be positioned to address.

Smooth pursuit operates over perceived not physical positions of the double-drift stimulus.

The double-drift illusion produces a large deviation in perceived direction that strongly dissociates physical position from perceived position. Surprisingly, saccades do not seem to be affected by the illusion (Lisi & Cavanagh, 2015). When targeting a double-drift stimulus, the saccade system is driven by retinal rather than perceived position. Here, using paired double-drift targets, we test whether the smooth pursuit system is driven by perceived or physical position. Participants (n = 7) smoothly pursued the inferred midpoint (Steinbach, 1976) between two horizontally aligned Gabor patches that were separated by 20° and moving on parallel, oblique paths. On the first half of each trial, the Gabors' internal textures were static while both drifted obliquely downward. On the second half of each trial, while the envelope moved obliquely upward, the internal texture drifted orthogonally to the envelope's motion, producing a large perceived deviation from the downward path even though the upward and downward trajectories always followed the same physical path but in opposite directions. We find that smooth pursuit eye movements accurately followed the nonillusory downward path of the midpoint between the two Gabors, but then followed the illusory rather than the physical trajectory on the upward return. Thus, virtual targets for smooth pursuit are derived from perceived rather than retinal coordinates.

Attentional tracking takes place over perceived rather than veridical positions.

Illusions can induce striking differences between perception and retinal input. For instance, a static Gabor with a moving internal texture appears to be shifted in the direction of its internal motion, a shift that increases dramatically when the Gabor itself is also in motion. Here, we ask whether attention operates on the perceptual or physical location of this stimulus. To do so, we generated an attentional tracking task where participants (N = 15) had to keep track of a single target among three Gabors that rotated around a common center in the periphery. During tracking, the illusion was used to make three Gabors appear either shifted away from or toward one another while maintaining the same physical separation. Because tracking performance depends in part on target to distractor spacing, if attention selects targets from perceived positions, performance should be better when the Gabors appear further apart and worse when they appear closer together. We find that tracking performance is superior with greater perceived separation, implying that attentional tracking operates over perceived rather than physical positions.

Object-based attention generalizes to multisurface objects.

When a part of an object is cued, targets presented in other locations on the same object are detected more rapidly and accurately than are targets on other objects. Often in object-based attention experiments, cues and targets appear not only on the same object but also on the same surface. In four psychophysical experiments, we examined whether the "object" of attentional selection was the entire object or one of its surfaces. In Experiment 1, facilitation effects were found for targets on uncued, adjacent surfaces on the same object, even when the cued and uncued surfaces were oriented differently in depth. This suggests that the "object-based" benefits of attention are not restricted to individual surfaces. Experiments 2a and 2b examined the interaction of perceptual grouping and object-based attention. In both experiments, cuing benefits extended across objects when the surfaces of those objects could be grouped, but the effects were not as strong as in Experiment 1, where the surfaces belonged to the same object. The cuing effect was strengthened in Experiment 3 by connecting the cued and target surfaces with an intermediate surface, making them appear to all belong to the same object. Together, the experiments suggest that the objects of attention do not necessarily map onto discrete physical objects defined by bounded surfaces. Instead, attentional selection can be allocated to perceptual groups of surfaces and objects in the same way as it can to a location or to groups of features that define a single object.