A typical scene contains many different objects that compete for neural representation due to the limited processing capacity of the visual system. At the neural level, competition among multiple stimuli is evidenced by the mutual suppression of their visually evoked responses and occurs most strongly at the level of the receptive field. The competition among multiple objects can be biased both exogenously, by bottom-up sensory-driven mechanisms, such as stimulus salience, and endogenously, by top-down influences, such as selective attention. Although the competition among stimuli for representation is ultimately resolved within visual cortex, the source of top-down biasing signals likely derives from a distributed network of areas in frontal and parietal cortex. Recently, we reported that monkeys with lesions of prefrontal cortex (PFC) are selectively impaired in their ability to switch top-down control. We next asked whether monkeys with lesions of posterior parietal cortex (PPC) would show similar or different behavioral effects. Our results showed that, unlike monkeys with PFC lesions, those with PPC lesions are not selectively impaired in their ability to switch top-down control. Rather, they have a selective impairment in spatially locating targets they are required to discriminate. Thus, the PFC plays a critical role in the ability to switch attentional control on the basis of changing task demands, whereas the PPC plays a critical role in allocating attentional resources to behaviorally relevant spatial locations. These findings are being prepared for publication.

 There is conflicting evidence in the literature regarding the role played by attention in perceptual learning. To further examine this issue, we independently manipulated exogenous (involuntary) and endogenous (voluntary) attention and measured the rate of perceptual learning of oriented stimuli presented in different quadrants of the visual field. In this way, we could track learning at attended, divided-attended, and unattended locations. We also measured contrast thresholds of the stimuli before and after training. Our results showed that, for both exogenous and endogenous attention, accuracy in performing the orientation discrimination improved to a greater extent at attended than at unattended locations. Importantly, however, only exogenous attention resulted in improved contrast thresholds. These findings suggest that both exogenous and endogenous attention facilitate perceptual learning, but that these two types of attention may be mediated by different neural mechanisms. A paper reporting these findings was published this past year. Retinotopic selectivity, as measured by fMRI activity patterns that vary consistently with the location of visual stimuli, has been documented in many human brain regions, notably occipital visual cortex and frontal and parietal regions associated with endogenous (voluntary) attention. We hypothesized that retinotopic selectivity also exists in regions active during exogenous (involuntary) attention. To test this hypothesis, we acquired fMRI data while subjects maintained passive fixation on a central cross. At unpredictable time intervals, stimuli consisting of an array of rapidly expanding circles appeared at one of six spatial locations. Positive fMRI activations to the stimulus presentations were identified in multiple brain regions including the temporoparietal junction (TPJ), a region previously implicated in exogenous attention. The TPJ activations did not appear to be organized as a map across the cortical surface. However, multivoxel pattern recognition analysis successfully predicted fMRI responses to every one of the fifteen stimulus location pairs, demonstrating that patterns of activity in TPJ depend on the retinotopic stimulus location. This is the first demonstration that spatial locations are represented in a brain region associated with exogenous attention. The data collection for this study is now complete. Threatening stimuli evoke exogenous (involuntary) attention and enhanced neural processing, but this can vary considerably even among healthy individuals. By manipulating expectation of threat in an event-related fMRI study of fearful vs. neutral face categorization, we aimed to better understand the spectrum of threat processing sensitivity. We altered expectation explicitly by presenting face images within runs containing three different proportions of fearful(F):neutral (N)faces: 80F:20N, 20F:80N, and 50F:50N, and subjects were instructed to report as fast and as accurately as possible whether the face was fearful (i.e. signaled threat) or not. A neutral cue preceded each face by 4 seconds (s). Overall subjects responded faster to the face type that was expected. However, across the subjects there was a spectrum of response times. By taking the difference in reaction time between responses to fearful and neutral faces, we quantified a fear reaction time bias (faster to fearful than neutral faces) for all subjects. This bias positively correlated with late trial fMRI activation during unexpected fearful face trials in the ventromedial prefrontal cortex bilaterally, the left subgenual cingulate cortex, and the right caudate nucleus and negatively correlated with early trial fMRI activation during expected neutral faces trials in the dorsal striatum bilaterally and in right ventral striatum. These results underscore that a large amount of variability exists in the processing of stimuli that signal threat in healthy adults, which is reflected not only in behavior but also in the magnitude of activation in brain regions implicated in value-based decision making. This study is being prepared for publication. It is well known that people take advantage of prior knowledge to bias decisions, that is, things they have experienced and attended in the past. To investigate this phenomenon behaviorally and in the brain, we acquired fMRI data while human subjects viewed ambiguous abstract shapes and decided whether a shape was of Category A (smoother) or B (bumpier). The decision was made in the context of one of two prior knowledge cues, 80/20 and 50/50. The 80/20 cue indicated that upcoming shapes had an 80% probability of being of one category, e.g., B, and a 20% probability of being of the other. The 50/50 cue indicated that upcoming shapes had an equal probability of being of either category. The ideal observer would bias decisions in favor of the indicated alternative at 80/20 and show zero bias at 50/50. We found that subjects did bias their decisions in the predicted direction at 80/20 but did not show zero bias at 50/50. Instead, at 50/50 the subjects retained biases of the same sign as their 80/20 biases, though of diminished magnitude. The signature of a persistent though diminished bias at 50/50 was also evident in fMRI data from frontal and parietal regions previously implicated in decision-making. Behavioral and fMRI data from nave subjects reflected decision biases closer to those of the ideal observer. The results indicate that practice making decisions in the context of non-equal prior probabilities biases decisions made later when prior probabilities are equal. This finding may be related to the """"""""anchoring and adjustment"""""""" strategy described in the psychology, economics, and marketing literatures, in which subjects adjust a first approximation response - the """"""""anchor"""""""" - based on additional information, typically applying insufficient adjustment relative to the ideal observer. A paper describing these findings was published in the past year.

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Taubert, Jessica; Wardle, Susan G; Flessert, Molly et al. (2017) Face Pareidolia in the Rhesus Monkey. Curr Biol 27:2505-2509.e2
Pitcher, David; Japee, Shruti; Rauth, Lionel et al. (2017) The Superior Temporal Sulcus Is Causally Connected to the Amygdala: A Combined TBS-fMRI Study. J Neurosci 37:1156-1161
Kaskan, P M; Costa, V D; Eaton, H P et al. (2016) Learned Value Shapes Responses to Objects in Frontal and Ventral Stream Networks in Macaque Monkeys. Cereb Cortex :
Bell, Andrew H; Summerfield, Christopher; Morin, Elyse L et al. (2016) Encoding of Stimulus Probability in Macaque Inferior Temporal Cortex. Curr Biol 26:2280-90
Zhang, Xilin; Japee, Shruti; Safiullah, Zaid et al. (2016) A Normalization Framework for Emotional Attention. PLoS Biol 14:e1002578
Zhang, Hui; Japee, Shruti; Nolan, Rachel et al. (2016) Face-selective regions differ in their ability to classify facial expressions. Neuroimage 130:77-90
Gattass, Ricardo; Lima, Bruss; Soares, Juliana G M et al. (2015) Controversies about the visual areas located at the anterior border of area V2 in primates. Vis Neurosci 32:E019
Zachariou, Valentinos; Nikas, Christine V; Safiullah, Zaid N et al. (2015) Common Dorsal Stream Substrates for the Mapping of Surface Texture to Object Parts and Visual Spatial Processing. J Cogn Neurosci 27:2442-61
Japee, Shruti; Holiday, Kelsey; Satyshur, Maureen D et al. (2015) A role of right middle frontal gyrus in reorienting of attention: a case study. Front Syst Neurosci 9:23
Debas, Karen; Carrier, Julie; Barakat, Marc et al. (2014) Off-line consolidation of motor sequence learning results in greater integration within a cortico-striatal functional network. Neuroimage 99:50-8

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