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Leite, A.

Publications and source records attributed to Leite, A..

2 recordsLinked to original sources

Multiple patterns of selectivity in superior colliculus control visual search

Visual search is a ubiquitous behavior for many animal species, but efficient search performance requires a relatively complex integration of target guidance and behavioral history signals. In this study, we investigate how the superior colliculus (SC), a midbrain area implicated in the control of overt spatial attention, integrates these two control signals in a multi-target search task. Monkeys made sequences of saccades to stimuli presented in a grid while searching for a "true" (reward eliciting) target amid color-coded targets and distractors. The efficient performance of this task requires integrating a target guidance signal with a history of previously fixated locations in order to guide search to new potential targets that might offer reward. Theories of systems neuroscience suggest that this multi-factor search behavior might be controlled using either mixed selectivity, dynamic selectivity, or pure selectivity. We find evidence for SC neurons exhibiting each of these patterns and conclude that this brain structure participates in circuits representing target similarity and previously fixated locations, in addition to a circuit responsible for integrating these signals using the mechanisms of dynamic and mixed selectivity. We also introduce a novel time-series partial information decomposition analysis that provides a rich and direct view of how a neuron allocates its representational capacity. These findings support an emerging view of the superior colliculus as much more than a bottom-up priority map, but instead an important site for the flexible cognitive control of visually-guided behavior.

neuroscience↗

Evaluating theories of neural information integration during visual search

The brain routes and integrates information from many sources during behavior. A number of models explain this phenomenon within the framework of mixed selectivity theory, yet it is difficult to compare their predictions to understand how neurons and circuits integrate information. In this work, we apply time-series partial information decomposition [PID] to compare models of integration on a dataset of superior colliculus [SC] recordings collected during a multi-target visual search task. On this task, SC must integrate target guidance, bottom-up salience, and previous fixation signals to drive attention. We find evidence that SC neurons integrate these factors in diverse ways, including decision-variable selectivity to expected value, functional specialization to previous fixation, and code-switching (to incorporate new visual input).

neuroscience↗