First-Pass Processing of Value Cues in the Ventral Visual Pathway

Neuronal Activity in the Gustatory Cortex during Economic Choice

An economic choice entails computing and comparing the values of individual offers. Offer values are represented in the orbitofrontal cortex (OFC)-an area that participates in value comparison-but it is unknown where offer values are computed in the first place. One possibility is that this computation takes place in OFC. Alternatively, offer values might be computed upstream of OFC. For choices between edible goods, a primary candidate is the gustatory region of the anterior insula (gustatory cortex, GC). Here we recorded from the GC of male rhesus monkeys choosing between different juice types. As a population, neurons in GC represented the flavor, the quantity, and the subjective value of the juice chosen by the animal. These variables were represented by distinct groups of cells and with different time courses. Specifically, chosen value signals emerged shortly after offer presentation, while neurons encoding the chosen juice and the chosen quantity peaked after juice delivery. Surprisingly, neurons in GC did not represent individual offer values in a systematic way. In a computational sense, the variables encoded in GC follow the process of value comparison. Thus our results argue against the hypothesis that offer values are computed in GC. At the same time, signals representing the subjective value of the expected reward indicate that responses in GC are not purely sensory. Thus neuronal responses in GC appear consummatory in nature.

Sex modulated the relationship between trait approach motivation and decision-making

It has been observed that one's Behavioral Approach System (BAS) can have an effect on decision-making under uncertainty, although the results have been mixed. To discern the underlying neural substrates, we hypothesize that sex may explain the conflicting results. To test this idea, a large sample of participants was studied using resting state fMRI, utilizing fractional Amplitude of Low Frequency Fluctuations (fALFF) and Resting-State Functional Connectivity (rsFC) techniques. The results of the Iowa Gambling Task (IGT) revealed an interaction between sex and BAS, particularly in the last 60 trials (decision-making under risk). Males with high BAS showed poorer performance than those with low BAS. fALFF analysis showed a significant interaction between BAS group and sex in the left superior occipital gyrus, as well as the functional connectivity between this region and the left ventrolateral prefrontal cortex. Additionally, this functional connectivity was further positively correlated with male performance in the IGT, particularly in the decision-making under risk stage. Furthermore, it was found that the functional connectivity between left ventrolateral prefrontal cortex and left superior occipital gyrus could mediate the relationship between BAS and decision-making in males, particularly in the decision-making under risk stage. These results suggest possible sex-based differences in decision-making, providing an explanation for the inconsistent results found in prior research. Since the research was carried out exclusively with Chinese university students, it is essential to conduct further studies to investigate whether the findings can be generalized.

An inferotemporal coding strategy robust to partial object occlusion

Object coding in primate ventral pathway cortex progresses in sparseness/compression/efficiency, from many orientation signals in V1, to fewer 2D/3D part signals in V4, to still fewer multi-part configuration signals in AIT (anterior inferotemporal cortex). 1-11 This progression could lead to individual neurons exclusively selective for unique objects, the sparsest code for identity, especially for highly familiar, important objects. 12-18 To test this, we trained macaque monkeys to discriminate 8 simple letter-like shapes in a match-to-sample task, a design in which one-to-one coding of letters by neurons could streamline behavior. Performance increased from chance to >80% correct over a period of weeks, after which AIT neurons showed clear learning effects, with increased selectivity for multi-part configurations within the trained alphabet shapes. But these neurons were not exclusively tuned for unique letters based on training, since their responsiveness generalized to different, non-trained shapes containing the same configurations. This multi-part configuration coding limit in AIT is not maximally sparse, but it could explain the robustness of primate vision to partial object occlusion, which is common in the natural world and problematic for computer vision. Multi-part configurations are highly diagnostic of identity, and neural signals for various partial object structures can provide different but equally sufficient evidence for whole object identity across most occlusion conditions.

Increasing associative plasticity in temporo-occipital back-projections improves visual perception of emotions

The posterior superior temporal sulcus (pSTS) is a critical node in a network specialized for perceiving emotional facial expressions that is reciprocally connected with early visual cortices (V1/V2). Current models of perceptual decision-making increasingly assign relevance to recursive processing for visual recognition. However, it is unknown whether inducing plasticity into reentrant connections from pSTS to V1/V2 impacts emotion perception. Using a combination of electrophysiological and neurostimulation methods, we demonstrate that strengthening the connectivity from pSTS to V1/V2 selectively increases the ability to perceive facial expressions associated with emotions. This behavior is associated with increased electrophysiological activity in both these brain regions, particularly in V1/V2, and depends on specific temporal parameters of stimulation that follow Hebbian principles. Therefore, we provide evidence that pSTS-to-V1/V2 back-projections are instrumental to perception of emotion from facial stimuli and functionally malleable via manipulation of associative plasticity.

Visual categories and concepts in the avian brain

Birds are excellent model organisms to study perceptual categorization and concept formation. The renewed focus on avian neuroscience has sparked an explosion of new data in the field. At the same time, our understanding of sensory and particularly visual structures in the avian brain has shifted fundamentally. These recent discoveries have revealed how categorization is mediated in the avian brain and has generated a theoretical framework that goes beyond the realm of birds. We review the contribution of avian categorization research-at the methodical, behavioral, and neurobiological levels. To this end, we first introduce avian categorization from a behavioral perspective and the common elements model of categorization. Second, we describe the functional and structural organization of the avian visual system, followed by an overview of recent anatomical discoveries and the new perspective on the avian 'visual cortex'. Third, we focus on the neurocomputational basis of perceptual categorization in the bird's visual system. Fourth, an overview of the avian prefrontal cortex and the prefrontal contribution to perceptual categorization is provided. The fifth section outlines how asymmetries of the visual system contribute to categorization. Finally, we present a mechanistic view of the neural principles of avian visual categorization and its putative extension to concept learning.

Neuronal origins of reduced accuracy and biases in economic choices under sequential offers

Economic choices are characterized by a variety of biases. Understanding their origins is a long-term goal for neuroeconomics, but progress on this front has been limited. Here, we examined choice biases observed when two goods are offered sequentially. In the experiments, rhesus monkeys chose between different juices offered simultaneously or in sequence. Choices under sequential offers were less accurate (higher variability). They were also biased in favor of the second offer (order bias) and in favor of the preferred juice (preference bias). Analysis of neuronal activity recorded in the orbitofrontal cortex revealed that these phenomena emerged at different computational stages. Lower choice accuracy reflected weaker offer value signals (valuation stage), the order bias emerged during value comparison (decision stage), and the preference bias emerged late in the trial (post-comparison). By neuronal measures, each phenomenon reduced the value obtained on average in each trial and was thus costly to the monkey.

Human Sensory Cortex Contributes to the Long-Term Storage of Aversive Conditioning

Growing animal data evince a critical role of the sensory cortex in the long-term storage of aversive conditioning, following acquisition and consolidation in the amygdala. Whether and how this function is conserved in the human sensory cortex is nonetheless unclear. We interrogated this question in a human aversive conditioning study using multidimensional assessments of conditioning and long-term (15 d) retention. Conditioned stimuli (CSs; Gabor patches) were calibrated to differentially activate the parvocellular (P) and magnocellular (M) visual pathways, further elucidating cortical versus subcortical mechanisms. Full-blown conditioning and long-term retention emerged for M-biased CS (vs limited effects for P-biased CS), especially among anxious individuals, in all four dimensions assessed: threat appraisal (threat ratings), physiological arousal (skin conductance response), perceptual learning [discrimination sensitivity (d') and response speed], and cortical plasticity [visual evoked potentials (VEPs) and cortical current density]. Interestingly, while behavioral, physiological, and VEP effects were comparable at immediate and delayed assessments, the cortical substrates evolved markedly over time, transferring from high-order cortices [inferotemporal/fusiform cortex and orbitofrontal cortex (OFC)] immediately to the primary and secondary visual cortex after the delay. In sum, the contrast between P- and M-biased conditioning confirms privileged conditioning acquisition via the subcortical pathway while the immediate cortical plasticity lends credence to the triadic amygdala-OFC-fusiform network thought to underlie threat processing. Importantly, long-term retention of conditioning in the basic sensory cortices supports the conserved role of the human sensory cortex in the long-term storage of aversive conditioning.SIGNIFICANCE STATEMENT A growing network of neural substrates has been identified in threat learning and memory. The sensory cortex plays a key role in long-term threat memory in animals, but such a function in humans remains unclear. To explore this problem, we conducted multidimensional assessments of immediate and delayed (15 d) effects of human aversive conditioning. Behavioral, physiological, and scalp electrophysiological data demonstrated conditioning effects and long-term retention. High-density EEG intracranial source analysis further revealed the cortical underpinnings, implicating high-order cortices immediately and primary and secondary visual cortices after the long delay. Therefore, while high-order cortices support aversive conditioning acquisition (i.e., threat learning), the human sensory cortex (akin to the animal homolog) underpins long-term storage of conditioning (i.e., long-term threat memory).

Mechanisms of competitive selection: A canonical neural circuit framework

Competitive selection, the transformation of multiple competing sensory inputs and internal states into a unitary choice, is a fundamental component of animal behavior. Selection behaviors have been studied under several intersecting umbrellas including decision-making, action selection, perceptual categorization, and attentional selection. Neural correlates of these behaviors and computational models have been investigated extensively. However, specific, identifiable neural circuit mechanisms underlying the implementation of selection remain elusive. Here, we employ a first principles approach to map competitive selection explicitly onto neural circuit elements. We decompose selection into six computational primitives, identify demands that their execution places on neural circuit design, and propose a canonical neural circuit framework. The resulting framework has several links to neural literature, indicating its biological feasibility, and has several common elements with prominent computational models, suggesting its generality. We propose that this framework can help catalyze experimental discovery of the neural circuit underpinnings of competitive selection.

Categorical Signaling of the Strongest Stimulus by an Inhibitory Midbrain Nucleus

The nucleus isthmi pars magnocellularis (Imc), a group of inhibitory neurons in the midbrain tegmentum, is a critical component of the spatial selection network in the vertebrate midbrain. It delivers long-range inhibition among different portions of the space map in the optic tectum (OT), thereby mediating stimulus competition in the OT. Here, we investigate the properties of relative strength-dependent competitive interactions within the Imc, in barn owls of both sexes. We find that when Imc neurons are presented simultaneously with one stimulus inside the receptive field and a second, competing stimulus outside, they exhibit gradual or switch-like response profiles as a function of relative stimulus strength. They do so both when the two stimuli are of the same sensory modality (both visual) or of different sensory modalities (visual and auditory). Moreover, Imc neurons signal the strongest stimulus in a dynamically flexible manner, indicating that Imc responses reflect an online comparison between the strengths of the competing stimuli. Notably, Imc neurons signal the strongest stimulus more categorically, and earlier than the OT. Paired recordings at spatially aligned sites in the Imc and OT reveal that although some properties of stimulus competition, such as the bias of competitive response profiles, are correlated, others such as the steepness of response profiles, are set independently. Our results demonstrate that the Imc is itself an active site of competition, and may be the first site in the midbrain selection network at which stimulus competition is resolved.SIGNIFICANCE STATEMENT This work sheds light on the functional properties of a small group of inhibitory neurons in the vertebrate midbrain that play a key part in how the brain selects a target among competitors. A better understanding of the functioning of these neurons is an important building block for the broader understanding of how distracters are suppressed, and of spatial attention and its dysfunction.

Learning in Visual Regions as Support for the Bias in Future Value-Driven Choice

Reinforcement learning can bias decision-making toward the option with the highest expected outcome. Cognitive learning theories associate this bias with the constant tracking of stimulus values and the evaluation of choice outcomes in the striatum and prefrontal cortex. Decisions however first require processing of sensory input, and to date, we know far less about the interplay between learning and perception. This functional magnetic resonance imaging study (N = 43) relates visual blood oxygen level-dependent (BOLD) responses to value beliefs during choice and signed prediction errors after outcomes. To understand these relationships, which co-occurred in the striatum, we sought relevance by evaluating the prediction of future value-based decisions in a separate transfer phase where learning was already established. We decoded choice outcomes with a 70% accuracy with a supervised machine learning algorithm that was given trial-by-trial BOLD from visual regions alongside more traditional motor, prefrontal, and striatal regions. Importantly, this decoding of future value-driven choice outcomes again highlighted an important role for visual activity. These results raise the intriguing possibility that the tracking of value in visual cortex is supportive for the striatal bias toward the more valued option in future choice.

Emotion schemas are embedded in the human visual system

Theorists have suggested that emotions are canonical responses to situations ancestrally linked to survival. If so, then emotions may be afforded by features of the sensory environment. However, few computational models describe how combinations of stimulus features evoke different emotions. Here, we develop a convolutional neural network that accurately decodes images into 11 distinct emotion categories. We validate the model using more than 25,000 images and movies and show that image content is sufficient to predict the category and valence of human emotion ratings. In two functional magnetic resonance imaging studies, we demonstrate that patterns of human visual cortex activity encode emotion category-related model output and can decode multiple categories of emotional experience. These results suggest that rich, category-specific visual features can be reliably mapped to distinct emotions, and they are coded in distributed representations within the human visual system.