Justin M. Ericson

The Effects of Incidentally Learned Temporal and Spatial Predictability on Response Times and Visual Fixations during Target Detection and Discrimination

Responses are quicker to predictable stimuli than if the time and place of appearance is uncertain. Studies that manipulate target predictability often involve overt cues to speed up response times. However, less is known about whether individuals will exhibit faster response times when target predictability is embedded within the inter-trial relationships. The current research examined the combined effects of spatial and temporal target predictability on reaction time (RT) and allocation of overt attention in a sustained attention task. Participants responded as quickly as possible to stimuli while their RT and eye movements were measured. Target temporal and spatial predictability were manipulated by altering the number of: 1) different time intervals between a response and the next target; and 2) possible spatial locations of the target. The effects of target predictability on target detection (Experiment 1) and target discrimination (Experiment 2) were tested. For both experiments, shorter RTs as target predictability increased across both space and time were found. In addition, the influences of spatial and temporal target predictability on RT and the overt allocation of attention were task dependent; suggesting that effective orienting of attention relies on both spatial and temporal predictability. These results indicate that stimulus predictability can be increased without overt cues and detected purely through inter-trial relationships over the course of repeated stimulus presentations.

Changing target trajectories influences tracking performance

People have the ability to attentively select and successfully track several moving objects, a process known as multiple-object tracking (MOT; Pylyshyn & Storm Spatial Vision 3: 179–197, 1988). Various factors have been known to influence MOT performance, such as speed, number of distractors, and proximity, while recent work has suggested that object trajectories may also be a factor (Fencsik, Kleiger, & Horowitz Perception and Psychophysics 69: 567–577, 2007). Meanwhile, unexpected changes in motion information have been demonstrated to be a critical facet for attracting attention Howard & Holcombe Attention, Perception & Psychophysics 72: 2087–2095, (2010). Therefore, we suggest that unexpected changes in target trajectories are an important factor in tracking performance. The research presented here controlled for spatial proximity while manipulating the number of instances in which an object changed trajectory. We found that spatial proximity had no effect on tracking performance but, rather, as the number of trajectory changes increased, tracking performance suffered. Results imply that the ability to track multiple moving objects is limited by unexpected changes in direction.

Attending Globally or Locally: Incidental Learning of Optimal Visual Attention Allocation.

Attention allocation determines the information that is encoded into memory. Can participants learn to optimally allocate attention based on what types of information are most likely to change? The current study examined whether participants could incidentally learn that changes to either high spatial frequency (HSF) or low spatial frequency (LSF) Gabor patches were more probable and to use this incidentally learned probability information to bias attention during encoding. Participants detected changes in orientation in arrays of 6 Gabor patches: 3 HSF and 3 LSF. For half of the participants, an HSF patch changed orientation on 75% of the trials, and for the other half, an LSF patch changed orientation on 75% of the trials. Experiment 1 demonstrated a change probability effect and an attention allocation effect. Specifically, change detection performance was highest for the probable-change type, and participants learned to use a global spread of attention (fixating between Gabor patches) when LSF patches were most likely to change and to use a local allocation of attention (fixating directly on Gabor patches) when HSF patches were most likely to change. Experiments 2 and 3 replicated these effects and demonstrated that an internal monitoring system is sufficient for these effects. That is, the effects do not require explicit feedback or point rewards. This study demonstrates that incidental learning of probability information can affect the allocation of attention during encoding and can therefore affect what information is stored in visual working memory.

Capture and tracking: Where does attention go?

Expected Findings • More changes in trajectory will lead to lower tracking accuracy. • If changes in trajectory capture attention. o Probe detection will be better for probes on items that have just changed trajectory. § Indicating attentional attraction/capture. o Probe detection will be better when more changes in trajectory occur. § Suggesting frequent reallocations of attention. o Reaction times will be faster to probes on items that have recently changed trajectory. § Suggesting frequent reallocations of attention. 3.