Autobiographical memory specificity in response to verbal and pictorial cues in clinical depression

Background Depressed individuals have been consistently shown to exhibit problems in accessing specific memories of events from their past and instead tend to retrieve categorical summaries of events. The majority of studies examining autobiographical memory changes associated with psychopathology have tended to use word cues, but only one study to date has used images (with PTSD patients). Objective to determine if using images to cue autobiographical memories would reduce the memory specificity deficit exhibited by patients with depression in comparison to healthy controls. Methods Twenty-five clinically depressed patients and twenty-five healthy controls were assessed on two versions of the autobiographical memory test; cued with emotional words and images. Results Depressed patients retrieved significantly fewer specific memories, and a greater number of categorical, than did the controls. Controls retrieved a greater proportion of specific memories to images compared to words, whereas depressed patients retrieved a similar proportion of specific memories to both images and words. Limitations no information about the presence and severity of past trauma was collected. Conclusions results suggest that the overgeneral memory style in depression generalises from verbal to pictorial cues. This is important because retrieval to images may provide a more ecologically valid test of everyday memory experiences than word-cued retrieval.

The influence of memory in a threshold model for distributed task assignment

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task selection in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without a priori knowledge of the available mail at the cities or inter-agent communication. In order to process a different mail type than the previous one, agents must undergo a change-over during which it remains inactive. We propose a threshold based algorithm in order to maximise the overall efficiency (the average amount of mail collected). We show that memory, i.e. the possibility for agents to develop preferences for certain cities, not only leads to emergent cooperation between agents, but also to a significant increase in efficiency (above the theoretical upper limit for any memoryless algorithm), and we systematically investigate the influence of the various model parameters. Finally, we demonstrate the flexibility of the algorithm to changes in circumstances, and its excellent scalability.