Individuals can learn by interacting with the environment and experiencing a difference between predicted and obtained outcomes (prediction error). However, many species also learn by observing the actions and outcomes of others. In contrast to individual learning, observational learning cannot be based on directly experienced outcome prediction errors. Accordingly, the behavioral and neural mechanisms of learning through observation remain elusive. Here we propose that human observational learning can be explained by two previously uncharacterized forms of prediction error, observational action prediction errors (the actual minus the predicted choice of others) and observational outcome prediction errors (the actual minus predicted outcome received by others). In a functional MRI experiment, we found that brain activity in the dorsolateral prefrontal cortex and the ventromedial prefrontal cortex respectively corresponded to these two distinct observational learning signals.
Combining transcranial magnetic stimulation (TMS) with concurrent functional magnetic resonance imaging (fMRI) allows study of how local brain stimulation may causally affect activity in remote brain regions. Here, we applied bursts of high- or low-intensity TMS over right posterior parietal cortex, during a task requiring sustained covert visuospatial attention to either the left or right hemifield, or in a neutral control condition, while recording blood oxygenation-level-dependent signal with a posterior MR surface coil. As expected, the active attention conditions activated components of the well-described "attention network," as compared with the neutral baseline. Also as expected, when comparing left minus right attention, or vice versa, contralateral occipital visual cortex was activated. The critical new finding was that the impact of high- minus low-intensity parietal TMS upon these visual regions depended on the currently attended side. High- minus low-intensity parietal TMS increased the difference between contralateral versus ipsilateral attention in right extrastriate visual cortex. A related albeit less pronounced pattern was found for left extrastriate visual cortex. Our results confirm that right human parietal cortex can exert attention-dependent influences on occipital visual cortex and provide a proof of concept for the use of concurrent TMS-fMRI in studying how remote influences can vary in a purely top-down manner with attentional demands.
Cognitive processes, such as spatial attention, are thought to rely on extended networks in the human brain. Both clinical data from lesioned patients and fMRI data acquired when healthy subjects perform particular cognitive tasks typically implicate a wide expanse of potentially contributing areas, rather than just a single brain area. Conversely, evidence from more targeted interventions, such as transcranial magnetic stimulation (TMS) or invasive microstimulation of the brain, or selective study of patients with highly focal brain damage, can sometimes indicate that a single brain area may make a key contribution to a particular cognitive process. But this in turn raises questions about how such a brain area may interface with other interconnected areas within a more extended network to support cognitive processes. Here, we provide a brief overview of new approaches that seek to characterise the causal role of particular brain areas within networks of several interacting areas, by measuring the effects of manipulations for a targeted area on function in remote interconnected areas. In human participants, these approaches include concurrent TMS-fMRI and TMS-EEG, as well as combination of the focal lesion method in selected patients with fMRI and/or EEG measures of the functional impact from the lesion on interconnected intact brain areas. Such approaches shed new light on how frontal cortex and parietal cortex modulate sensory areas in the service of attention and cognition, for the normal and damaged human brain.
Reward can influence visual performance, but the neural basis of this effect remains poorly understood. Here we used functional magnetic resonance imaging to investigate how rewarding feedback affected activity in distinct areas of human visual cortex, separating rewarding feedback events after correct performance from preceding visual events. Participants discriminated oriented gratings in either hemifield, receiving auditory feedback at trial end that signaled financial reward after correct performance. Greater rewards improved performance for all but the most difficult trials. Rewarding feedback increased blood-oxygen-level-dependent (BOLD) signals in striatum and orbitofrontal cortex. It also increased BOLD signals in visual areas beyond retinotopic cortex, but not in primary visual cortex representing the judged stimuli. These modulations were seen at a time point in which no visual stimuli were presented or expected, demonstrating a novel type of activity change in visual cortex that cannot reflect modulation of response to incoming or anticipated visual stimuli. Rewarded trials led on the next trial to improved performance and enhanced visual activity contralateral to the judged stimulus, for retinotopic representations of the judged visual stimuli in V1. Our findings distinguish general effects in nonretinotopic visual cortex when receiving rewarding feedback after correct performance from consequences of reward for spatially specific responses in V1.
Interplay between the cerebral hemispheres is vital for coordinating perception and behavior. One influential account holds that the hemispheres engage in rivalry, each inhibiting the other. In the somatosensory domain, a seminal paper claimed to demonstrate such interhemispheric rivalry, reporting improved tactile detection sensitivity on the right hand after transcranial magnetic stimulation (TMS) to the right parietal lobe (Seyal, Ro, & Rafal, 1995). Such improvement in tactile detection ipsilateral to TMS could follow from interhemispheric rivalry, if one assumes that TMS disrupted cortical processing under the coil and thereby released the other hemisphere from inhibition. Here we extended the study by Seyal et al. (1995) to determine the effects of right parietal TMS on tactile processing for either hand, rather than only the ipsilateral hand. We performed two experiments applying TMS in the context of median-nerve stimulation; one experiment required somatosensory detection, the second somatosensory intensity discrimination. We found different TMS effects on detection versus discrimination, but neither set of results followed the prediction from hemispheric rivalry that enhanced performance for one hand should invariably be associated with impaired performance for the other hand, and vice-versa. Our results argue against a strict rivalry interpretation, instead suggesting that parietal TMS can provide a pedestal-like increment in somatosensory response.
Contralesional dorsal premotor cortex (cPMd) may support residual motor function following stroke. We performed two complementary experiments to explore how cPMd might perform this role in a group of chronic human stroke patients. First, we used paired-coil transcranial magnetic stimulation (TMS) to establish the physiological influence of cPMd on ipsilesional primary motor cortex (iM1) at rest. We found that this influence became less inhibitory/more facilitatory in patients with greater clinical impairment. Second, we applied TMS over cPMd during functional magnetic resonance imaging (fMRI) in these patients to examine the causal influence of cPMd TMS on the whole network of surviving cortical motor areas in either hemisphere and whether these influences changed during affected hand movement. We confirmed that hand grip-related activation in cPMd was greater in more impaired patients. Furthermore, the peak ipsilesional sensorimotor cortex activity shifted posteriorly in more impaired patients. Critical new findings were that concurrent TMS-fMRI results correlated with the level of both clinical impairment and neurophysiological impairment (i.e., less inhibitory/more facilitatory cPMd-iM1 measure at rest as assessed with paired-coil TMS). Specifically, greater clinical and neurophysiological impairment was associated with a stronger facilitatory influence of cPMd TMS on blood oxygenation level-dependent signal in posterior parts of ipsilesional sensorimotor cortex during hand grip, corresponding to the posteriorly shifted sensorimotor activity seen in more impaired patients. cPMd TMS was not found to influence activity in other brain regions in either hemisphere. This state-dependent influence on ipsilesional sensorimotor regions may provide a mechanism by which cPMd supports recovered function after stroke.
With rapid urban expansion and loss of open space, attractive local landscapes will continue to gain
importance in location decisions and on political agendas. The present study reviews the evidence on the local economic role of landscape amenities from two major strands of empirical research, migration and regional economic models, and hedonic pricing models. Following common amenity definitions we identify 71 relevant peer-reviewed studies and systematically assess the reported effects of the landscape amenity variables. The migration and regional economic studies suggest that migrants are attracted by amenities nearly as often as by low taxes. Reported effects of amenities on income and employment are less consistent.
The hedonic studies suggest that nature reserves and land cover diversity have mostly, open space and forest often, and agricultural land rarely positive effects on housing prices. Studies at larger geographic scales and studies involving urban areas were more likely to identify significant amenity effects. Some limitations of the
evidence may be overcome with better datasets and modeling approaches. However, in line with other recent work, the limitations also highlight the need for complementary information from the analysis of political preferences for land-use management.
This article reviews important concepts and methods that are useful for hypothesis testing. First, we discuss the Neyman-Pearson framework. Various approaches to optimality are presented, including finite-sample and large-sample optimality. Then, we summarize some of the most important methods, as well as resampling methodology, which is useful to set critical values. Finally, we consider the problem of multiple testing, which has witnessed a burgeoning literature in recent years. Along the way, we incorporate some examples that are current in the econometrics literature. While many problems with well-known successful solutions are included, we also address open problems that are not easily handled with current technology, stemming from such issues as lack of optimality or poor asymptotic approximations.
