Learning and memory impairments are present in schizophrenia (SZ) throughout the illness course and predict psychosocial function. Abnormalities in prefrontal and hippocampal function are thought to contribute to SZ deficits. The radial arm maze (RAM) is a test of spatial learning and memory in rodents that relies on intact prefrontal and hippocampal function. The goal of the present study was to investigate spatial learning in SZ using a virtual RAM. Thirty-three subjects with SZ and thirty-nine healthy controls (HC) performed ten trials of a virtual RAM task. Subjects attempted to learn to retrieve four rewards each located in separate arms. As expected, subjects with SZ used more time and traveled more distance to retrieve rewards, made more reference (RM) and working memory (WM) errors, and retrieved fewer rewards than HC. It is important to note that the SZ group did learn but did not reach the level of HC. Whereas RM errors decreased across trials in the SZ group, WM errors did not. There were no significant relationships between psychiatric symptom severity and maze performance. To our knowledge, use of a virtual 8-arm radial maze task in SZ to assess spatial learning is novel. Impaired virtual RAM performance in SZ is consistent with studies that examined RAM performance in animal models of SZ. Results provide further support for compromised prefrontal and hippocampal function underlying WM and RM deficits in SZ. The virtual RAM task could help bridge preclinical and clinical research for testing novel drug treatments of SZ.

The preliminary results of our study in civilians with PTSD replicate previous MRS studies and are consistent with decreased hippocampal neuronal integrity without effects in the OWM. Replication of our findings is needed.

The population of linear experts (POLE) model suggests that function learning and transfer are mediated by activation of a set of prestored linear functions that together approximate the given function (Kalish, Lewandowsky, & Kruschke, 2004). In the extrapolation-association (EXAM) model, an exemplar-based architecture associates trained input values with their paired output values. Transfer incorporates a linear rule-based response mechanism (McDaniel & Busemeyer, 2005). Learners were trained on a functional relationship defined by 2 linear-function segments with mirror slopes. In Experiment 1, 1 segment was densely trained and 1 was sparsely trained; in Experiment 2, both segments were trained equally, but the 2 segments were widely separated. Transfer to new input values was tested. For each model, training performance for each individual participant was fit, and transfer predictions were generated. POLE generally better fit the training data than did EXAM, but EXAM was more accurate at predicting (and fitting) transfer behaviors. It was especially telling that in Experiment 2 the transfer pattern was more consistent with EXAM's but not POLE's predictions, even though the presentation of salient linear segments during training dovetailed with POLE's approach.

Relational learning, which is learning the relationship among items, is impaired in schizophrenia but can be improved with training. This study investigated neural changes with functional magnetic resonance imaging before and after training on a relational learning task in schizophrenia and healthy control subjects. Despite their acquiring similar relational learning performance, the groups exhibited different neural activation patterns before and following training. Controls engaged regions within the relational learning network that included frontal, parietal, and medial temporal lobe, before and following training. Controls also exhibited activation reductions in region and spatial extent with relational learning proficiency, a commonly observed phenomenon in successful learning. In contrast, subjects with schizophrenia displayed no positive activations compared with the control condition before training. After training, subjects with schizophrenia displayed bilateral inferior parietal region activation as predicted. Contrary to hypothesis, hippocampal activation was not observed following training in schizophrenia. These findings suggest that the parietal lobe may be receptive to cognitive training interventions and that successful relational learning may be achieved in schizophrenia through the use of alternative extrahippocampal brain regions.

Unusual language use is a core feature of psychosis, but the nature and significance of this are not understood. In particular, thought disorder in schizophrenia is characterized by markedly bizarre speech, but the cognitive components that contribute to this and the brain correlates of these components are unknown. A number of studies have demonstrated language abnormalities in single-word processing, but few have examined speech in schizophrenia at the discourse level. This has been at least partly due to the difficulty in quantifying content of discourse. Recently, methods in computational linguistics have been found to be useful for detecting differences in semantic coherence during discourse between different clinical groups. We build on this work by demonstrating how these methods can be combined with fMRI in order to tease apart factors that underlie free discourse and its deviations, and how they relate to brain activity. Eleven volunteers with schizophrenia and eleven controls participated in an interview during which they were asked to talk as much as they could about ‘religious belief’. These same participants underwent fMRI during a word monitoring task, during which modality of monitoring was manipulated by varying the congruence of auditory and visual stimuli. Semantic coherence scores, measured from free discourse, were examined for their relationship to brain activations during fMRI. In healthy controls, regions associated with executive function were related to coherence. In persons with schizophrenia, coherence was mainly related to auditory and visual regions, depending on the modality of monitoring, but superior/middle temporal cortex related to coherence regardless of task. These findings are consistent with existing evidence for a role of superior temporal cortex in thought disorder, and demonstrate that computational measures of semantic content capture objective measures of coherence in speech that can be usefully related to underlying neurophysiological processes.