Biology subjects

Crossley, M. J.

Publications and source records attributed to Crossley, M. J..

4 recordsLinked to original sources

bioRxiv2024

Criterial Learning and Feedback Delay: Insights from Computational Models and Behavioral Experiments

The notion of a response criterion is ubiquitous in psychology, yet its cognitive and neural underpinnings remain poorly understood. To address this shortcoming, three computational models that capture different hypotheses about criterial learning were developed and tested. The time-dependent drift model assumes the criterion is stored in working memory and that its value drifts over time. The delay-sensitive learning model assumes that the magnitude of criterial learning is temporally discounted by feedback delay. The reinforcement-learning model assumes that criterial learning emerges from stimulus-response association learning without an explicit representation of the criterion, with learning rate also temporally discounted by feedback delay. The performance of these models was investigated under varying feedback delay and intertrial interval (ITI) durations. The time-dependent drift model predicted that long ITIs and feedback delays both impair criterial learning. In contrast, the delay-sensitive and reinforcement-learning models predicted impairments only with feedback delays. Two behavioral experiments, which tested these predictions, showed that human criterial learning is impaired by delayed feedback but not by long ITIs. These results support the delay-sensitive and reinforcement-learning models, and suggest that even in tasks that appear to rely on explicit, rule-based reasoning, criterial learning may have strong associative underpinnings.

neuroscience↗

bioRxiv2023

Context versus aiming in motor learning when both feedforward and feedback control processes are engaged

Theories of human motor learning commonly assume that movement plans are adjusted in response to the precision of sensory feedback received regarding their success. However, support for this assumption has mainly come from experiments that limit feedback correction during an ongoing movement. In contrast, we have recently shown that when this restriction is relaxed, and both within-movement and between-movement corrections can occur, movement plans undergo large and abrupt changes that are strongly correlated with the degree of sensory uncertainty present on the previous trial and are insensitive to the magnitude and direction of recently experienced movement errors. A class of models in which sensory uncertainty influences an aiming process with no retention from one trial to the next best accounted for these data. Here, we examine an alternative possibility that sensory uncertainty acts as a contextual cue to shunt motor learning and control to one of many context-specific internal models. Although both aiming and context models provide good fits for our data, the aiming model performed best. Author summaryA large body of literature shows that sensory uncertainty inversely scales the degree of error-driven corrections made to motor plans from one trial to the next. However, by limiting sensory feedback to the endpoint of movements, these studies prevent corrections from taking place during the movement. We have recently shown that when such corrections are permitted, sensory uncertainty punctuates between-trial movement corrections with abrupt changes that closely track the degree of sensory uncertainty but are insensitive to the magnitude and direction of recently experienced movement error. Here, we ask whether this pattern of behaviour is more consistent with sensory uncertainty driving changes in an aiming process or context-specific motor learning.

neuroscience↗

bioRxiv2023

Sensory uncertainty influences motor learning differently in blocked versus interleaved trial contexts when both feedforward and feedback processes are engaged

Theories of human motor learning commonly assume that the degree to which movement plans are adjusted in response to movement errors scales with the precision of sensory feedback received regarding their success. However, support for such error-scaling models has mainly come from experiments that limit the amount of correction that can occur within an ongoing movement. In contrast, we have recently shown that when this restriction is relaxed, and both within-movement and between-movement corrections co-occur, movement plans undergo large and abrupt changes that are strongly correlated with the degree of sensory uncertainty present on the previous trial and are insensitive to the magnitude and direction of the experienced movement error. Here, we show that the presence of these abrupt and error-insensitive changes can only be reliably detected when different levels of sensory precision are interleaved pseudo randomly on a trial-by-trial basis. These results augment our earlier findings and suggest that the co-occurrence of within-movement and between-movement corrections is not the only important aspect of our earlier study that challenged the error-scaling models of motor learning under uncertainty. Author summaryA large body of literature shows that sensory uncertainty inversely scales the degree of error-driven corrections made to motor plans from one trial to the next. However, by limiting sensory feedback to the endpoint of movements, these studies prevent corrections from taking place during the movement. We have recently shown that when such corrections are promoted, sensory uncertainty punctuates between-trial movement corrections with abrupt changes that closely track the degree of sensory uncertainty but are insensitive to the magnitude and direction of movement error. Here, we show that this result requires different levels of sensory uncertainty to be mixed on a trial-by-trial basis. This carries important implications for how previous studies of motor learning under uncertainty are interpreted, and what future studies will likely constitute progress for the field.

neuroscience↗

bioRxiv2023

Partial information transfer from peripheral visual streams to foveal visual streams is mediated through local primary visual circuits

A classic view holds that visual object recognition is driven through the what pathway in which perceptual features of increasing abstractness are computed in a sequence of different visual cortical regions. The cortical origin of this pathway, the primary visual cortex (V1), has a retinotopic organization such that neurons have receptive fields tuned to specific regions of the visual field. That is, a neuron that responds to a stimulus in the center of the visual field will not respond to a stimulus in the periphery of the visual field, and vice versa. However, despite this fundamental design feature, the overall processing of stimuli in the periphery - while clearly dependent on processing by neurons in the peripheral regions of V1 - can be clearly altered by the processing of neurons in the fovea region of V1. For instance, it has been shown that task-relevant, non-retinotopic feedback information about peripherally presented stimuli can be decoded in the unstimulated foveal cortex, and that the disruption of this feedback - through Transcranial Magnetic Stimulation or behavioral masking paradigms - has detrimental effects on same/different discrimination behavior. Here, we used fMRI multivariate decoding techniques and functional connectivity analyses to assess the nature of the information that is encoded in the periphery-to-fovea feedback projection and to gain insight into how it may be anatomically implemented. Participants performed a same/different discrimination task on images of real-world stimuli (motorbikes, cars, female and male faces) displayed peripherally. We were able to decode only a subset of these categories from the activity measured in peripheral V1, and a further reduced subset from the activity measured in foveal V1, indicating that the feedback from periphery to fovea may be subject to information loss. Functional connectivity analysis revealed that foveal V1 was functionally connected only to the peripheral V1 and not to later-stage visual areas, indicating that the feedback from peripheral to foveal V1 is likely implemented by neural circuits local to V1.

neuroscience↗