Krishna P. Miyapuram

fMRI investigation of cortical and subcortical networks in the learning of abstract and effector-specific representations of motor sequences

A visuo-motor sequence can be learned as a series of visuo-spatial cues or as a sequence of effector movements. Earlier imaging studies have revealed that a network of brain areas is activated in the course of motor sequence learning. However, these studies do not address the question of the type of representation being established at various stages of visuo-motor sequence learning. In an earlier behavioral study, we demonstrated that acquisition of visuo-spatial sequence representation enables rapid learning in the early stage and progressive establishment of somato-motor representation helps speedier execution by the late stage. We conducted functional magnetic resonance imaging (fMRI) experiments wherein subjects learned and practiced the same sequence alternately in normal and rotated settings. In one rotated setting (visual), subjects learned a new motor sequence in response to an identical sequence of visual cues as in normal. In another rotated setting (motor), the display sequence was altered as compared to normal, but the same sequence of effector movements was used to perform the sequence. Comparison of different rotated settings revealed analogous transitions both in the cortical and subcortical sites during visuo-motor sequence learning-a transition of activity from parietal to parietal-premotor and then to premotor cortex and a concomitant shift was observed from anterior putamen to a combined activity in both anterior and posterior putamen and finally to posterior putamen. These results suggest a putative role for engagement of different cortical and subcortical networks at various stages of learning in supporting distinct sequence representations.

BOLD responses in reward regions to hypothetical and imaginary monetary rewards

Monetary rewards are uniquely human. Because money is easy to quantify and present visually, it is the reward of choice for most fMRI studies, even though it cannot be handed over to participants inside the scanner. A typical fMRI study requires hundreds of trials and thus small amounts of monetary rewards per trial (e.g. 5p) if all trials are to be treated equally. However, small payoffs can have detrimental effects on performance due to their limited buying power. Hypothetical monetary rewards can overcome the limitations of smaller monetary rewards but it is less well known whether predictors of hypothetical rewards activate reward regions. In two experiments, visual stimuli were associated with hypothetical monetary rewards. In Experiment 1, we used stimuli predicting either visually presented or imagined hypothetical monetary rewards, together with non-rewarding control pictures. Activations to reward predictive stimuli occurred in reward regions, namely the medial orbitofrontal cortex and midbrain. In Experiment 2, we parametrically varied the amount of visually presented hypothetical monetary reward keeping constant the amount of actually received reward. Graded activation in midbrain was observed to stimuli predicting increasing hypothetical rewards. The results demonstrate the efficacy of using hypothetical monetary rewards in fMRI studies.

Changing the structure of complex visuo-motor sequences selectively activates the fronto-parietal network.

Previous brain imaging studies investigating motor sequence complexity have mainly examined the effect of increasing the length of pre-learned sequences. The novel contribution of this research is that we varied the structure of complex visuo-motor sequences along two different dimensions using mxn paradigm. The complexity of sequences is increased from 12 movements (organized as a 2×6 task) to 24 movements (organized as 4×6 and 2×12 tasks). Behavioral results indicate that although the success rate attained was similar across the two complex tasks (2×12 and 4×6), a greater decrease in response times was observed for the 2×12 compared to the 4×6 condition at an intermediate learning stage. This decrease is possibly related to successful chunking across sets in the 2×12 task. In line with this, we observed a selective activation of the fronto-parietal network. Shifts of activation were observed from the ventral to dorsal prefrontal, lateral to medial premotor and inferior to superior parietal cortex from the early to intermediate learning stage concomitant with an increase in hyperset length. We suggest that these selective activations and shifts in activity during complex sequence learning are possibly related to chunking of motor sequences.

Meta-analysis of functional neuroimaging data

Functional neuroimaging offers huge amounts of data that require computational tools to help extract useful information about brain function. The ever increasing number of neuroimaging studies (above 5000 in 2012 alone) suggests the need for a meta-analysis of these findings. Meta-analysis is aimed at increasing the power and reliability of findings from individual studies. Currently, two methods of meta-analyses are the most popular in brain imaging literature. The coordinate based meta-analysis (CBMA) which refers to the maximum likelihood of brain activation based on a universal three dimensional coordinate system. The image based meta-analysis (IBMA) which considers the effect sizes from different studies to increase statistical power ignoring the inter-study consistency requirements. This technique is, however, suitable to account for inter-subject variability either pooled over studies or including the inter-study variability. While the coordinate based meta-analysis is easily found through published literature, the image based analysis requires the statistical parametric maps available. These Data mining techniques applied in brain imaging is often termed as the new paradigm in cognitive neuroscience. We here discuss in detail about the available analysis methods.

Understanding decision neuroscience: a multidisciplinary perspective and neural substrates.

The neuroscience of decision making is a rapidly evolving multidisciplinary research area that employs neuroscientific techniques to explain various parameters associated with decision making behavior. In this chapter, we emphasize the role of multiple disciplines such as psychology, economics, neuroscience, and computational approaches in understanding the phenomenon of decision making. Further, we present a theoretical approach that suggests understanding the building blocks of decision making as bottom-up processes and integrate these with top-down modulatory factors. Relevant neurophysiological and neuroimaging findings that have used the building-block approach are reviewed. A unifying framework emphasizing multidisciplinary views would bring further insights into the active research area of decision making. Pointing to future directions for research, we focus on the role of computational approaches in such a unifying framework.

Chunking Phenomenon in Complex Sequential Skill Learning in Humans

Sequential skill learning is central to much of human behaviour. It is known that sequences are hierarchically organized into several chunks of information that enables efficient performance of the acquired skill. We present clustering analysis on response times as subjects learn finger movement sequences of length 24 arranged in two ways – 12 sets of two movements each and 6 sets of four movements each. The experimental results and the analysis point out that greater amount of reorganization of sequences into chunks is more likely when the set-size is kept lower and discuss the cognitive implications of these findings.

Feedback about action performed can alter the sense of self-agency

Sense of agency refers to the sense of authorship of an action and its outcome. Sense of agency is often explained through computational models of motor control (e.g., the comparator model). Previous studies using the comparator model have manipulated action-outcome contingency to understand its effect on the sense of agency. More recent studies have shown that cues related to outcome, priming outcome and priming action have an effect on agency attribution. However, relatively few studies have focused on the effect of recalibrating internal predictions on the sense of agency. This study aims to investigate how feedback about action can recalibrate prediction and modulates the sense of agency. While participants performed a Flanker task, we manipulated the feedback about the validity of the action performed, independent of their responses. When true feedback is given, the sense of agency would reflect congruency between the sensory outcome and the action performed. The results show an opposite effect on the sense of agency when false feedback was given. We propose that feedback about action performed can recalibrate the prediction of sensory outcome and thus alter the sense of agency.

Hierarchical Chunking during Learning of Visuomotor Sequences

It is well known that learning a sequential skill involves chaining a number of primitive actions together into chunks. We describe three different experiments using an explicit visuomotor sequence learning paradigm called the m times n task. The m times n task enables hierarchical learning of sequences by presenting m elements of the sequence at a time (called the set). The entire sequence to be learned is composed of n such sets and is called a hyperset. In the first experiment, we showed the chunking phenomenon while learning a sequence as opposed to following randomly generated visual cues. We further explored the nature of chunking across sets using complex sequences in the second experiment. Finally, we investigated effector dependence of the chunking patterns in the third experiment. Our results point out the facilitating factors for chunk formation in visuomotor sequence learning.

Understanding consumer decisions using behavioural economics

People make many decisions throughout the day involving finances, food and health. Many of these decisions involve considering alternatives that will occur at some point in the future. Behavioural economics is a field that studies how people make these decisions (Camerer, 1999)[[Au: The reference “Camerer (1999)” is cited in the text but not listed. Please check.]]. It shows that people are driven by short-term gratification (reward or benefit). For example: given a choice between choosing

Predicting the imagined contents using brain activation

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