Decision-making about affective value may occur after the reward value of a stimulus is represented and may involve different brain areas to those involved in decision-making about the physical properties of stimuli, such as intensity. In an fMRI study, we delivered two odors separated by a delay, with instructions on different trials to decide which odor was more pleasant or more intense or to rate the pleasantness and intensity of the second odor without making a decision. The fMRI signals in the medial prefrontal cortex area 10 (medial PFC) and in regions to which it projects, including the anterior cingulate cortex (ACC) and insula, were higher when decisions were being made compared with ratings, implicating these regions in decision-making. Decision-making about affective value was related to larger signals in the dorsal part of medial area 10 and the agranular insula, whereas decisions about intensity were related to larger activations in the dorsolateral prefrontal cortex (dorsolateral PFC), ventral premotor cortex, and anterior insula. For comparison, the mid orbitofrontal cortex (OFC) had activations related not to decision-making but to subjective pleasantness ratings, providing a continuous representation of affective value. In contrast, areas such as medial area 10 and the ACC are implicated in reaching a decision in which a binary outcome is produced.

How does selective attention to affect influence sensory processing? In a functional magnetic resonance imaging investigation, when subjects were instructed to remember and rate the pleasantness of a jasmin odor, activations were greater in the medial orbito-frontal and pregenual cingulate cortex than when subjects were instructed to remember and rate the intensity of the odor. When the subjects were instructed to remember and rate the intensity, activations were greater in the inferior frontal gyrus. These top-down effects occurred not only during odor delivery but started in a preparation period after the instruction before odor delivery, and continued after termination of the odor in a short-term memory period. Thus, depending on the context in which odors are presented and whether affect is relevant, the brain prepares itself, responds to, and remembers an odor differently. These findings show that when attention is paid to affective value, the brain systems engaged to prepare for, represent, and remember a sensory stimulus are different from those engaged when attention is directed to the physical properties of a stimulus such as its intensity. This differential biasing of brain regions engaged in processing a sensory stimulus depending on whether the cognitive demand is for affect-related versus more sensory-related processing may be an important aspect of cognition and attention. This has many implications for understanding the effects not only of olfactory but also of other sensory stimuli.

A key issue in the neurophysiology of cognition is the problem of sequential learning. Sequential learning refers to the ability to encode and represent the temporal order of discrete elements occurring in a sequence. We show that the short-term memory for a sequence of items can be implemented in an autoassociation neural network. Each item is one of the attractor states of the network. The autoassociation network is implemented at the level of integrate-and-fire neurons so that the contributions of different biophysical mechanisms to sequence learning can be investigated. It is shown that if it is a property of the synapses or neurons that support each attractor state that they adapt, then everytime the network is made quiescent (e.g., by inhibition), then the attractor state that emerges next is the next item in the sequence. We show with numerical simulations implementations of the mechanisms using (1) a sodium inactivation-based spike-frequency-adaptation mechanism, (2) a Ca 2+ -activated K + current, and (3) short-term synaptic depression, with sequences of up to three items. The network does not need repeated training on a particular sequence and will repeat the items in the order that they were last presented. The time between the items in a sequence is not fixed, allowing the items to be read out as required over a period of up to many seconds. The network thus uses adaptation rather than associative synaptic modification to recall the order of the items in a recently presented sequence.

Single-neuron recordings, functional magnetic resonance imaging (fMRI) data, and the effects of lesions indicate that the prefrontal cortex (PFC) is involved in some types of working memory and related cognitive processes. Based on these data, two different models of the topographical and functional organization of the PFC have been proposed: organization-by-stimulus-domain, and organization-by-process. In this article, we utilize an integrate-and-fire network to model both single-neuron and fMRI data on short-term memory in order to understand data obtained in topologically different parts of the PFC during working memory tasks. We explicitly model the mechanisms that underlie workingmemory-related activity during the execution of delay tasks that have a “what”-then-“where” design (with both object and spatial delayed responses within the same trial). The model contains different populations of neurons (as found experimentally) in attractor networks that respond in the delay period to the stimulus object, the stimulus position, and to combinations of both object and position information. The pools are arranged hierarchically and have global inhibition through inhibitory interneurons to implement competition. It is shown that a biasing attentional input to define the current relevant information (object or location) enables the system to select the correct neuronal populations during the delay period in what is a biased competition model of attention. The processes occurring at the AMPA and NMDA synapses are dynamically modeled in the integrate-and-fire implementation to produce realistic spiking dynamics. It is shown that the fMRI data characteristic of the dorsal PFC and linked to spatial processing and manipulation of items can be reproduced in the model by a high level of inhibition, whereas the fMRI data characteristic of the ventral PFC and linked to object processing can be produced by a lower level of inhibition, even though the network is itself topographically homogeneous with no spatial topology of the neurons. This article, thus, not only presents a model for how spatial versus object short-term memory could be implemented in the PFC, but also shows that the fMRI BOLD signal measured during such tasks from different parts of the PFC could reflect a higher level of inhibition dorsally, without this dorsal region necessarily being primarily spatial and the ventral region object-related.

Backward masking can potentially provide evidence of the time needed for visual processing, a fundamental constraint that must be incorporated into computational models of vision. Although backward masking has been extensively used psychophysically, there is little direct evidence for the effects of visual masking on neuronal responses. To investigate the effects of a backward masking paradigm on the responses of neurons in the temporal visual cortex, we have shown that the response of the neurons is interrupted by the mask. Under conditions when humans can just identify the stimulus, with stimulus onset asynchronies (SOA) of 20 msec, neurons in macaques respond to their best stimulus for approximately 30 msec. We now quantify the information that is available from the responses of single neurons under backward masking conditions when two to six faces were shown. We show that the information available is greatly decreased as the mask is brought closer to the stimulus. The decrease is more marked than the decrease in firing rate because it is the selective part of the firing that is especially attenuated by the mask, not the spontaneous firing, and also because the neuronal response is more variable at short SOAs. However, even at the shortest SOA of 20 msec, the information available is on average 0.1 bits. This compares to 0.3 bits with only the 16-msec target stimulus shown and a typical value for such neurons of 0.4 to 0.5 bits with a 500-msec stimulus. The results thus show that considerable information is available from neuronal responses even under backward masking conditions that allow the neurons to have their main response in 30 msec. This provides evidence for how rapid the processing of visual information is in a cortical area and provides a fundamental constraint for understanding how cortical information processing operates.