Confound controls

A series of further analyses were performed to exclude further confounds and validate the appropriateness of the data analysis methods. These analyses investigate the overall fMRI responsiveness, patterns in observers' eye movements, and the power of the MVPA

classification.

The analysis of the fMRI responsivness was conducted to identify differences across sessions which could result from differences in the difficulty of the task. The results of an analysis of the percent signal change from a baseline of the fixation condition across cortical regions suggested that the differences in tuning found were not due to differences in overall fMRI signal, since no significant difference in the fMRI responsiveness across sessions was found (F(2,14) = 1.08, p = 0.37).

To account for the possibility of the effects of eye-movement on the results, eye-tracking was performed during the scanning sessions. Analysis of the collected data, shown in Figure S5 from Zhang et al. (2010), did not show a significant difference between sessions, suggesting that eye movement differences did not contribute significantly to the difference in tuning found between sessions.

Finally, to validate the classification methodology and ensure that it was neither biased or over-powered, a shuffling analysis was performed, in which the six-way classification

analysis was applied to shuffled data labels. This was performed for 1000 permutations of the data labels, and the mean prediction accuracies from these 1000 iterations were found not to differ significantly from the expected chance level of 0.167, as shown in Table S1 from Zhang et al. (2010) which is included in Appendix 1.

4.4 Discussion

The findings reported in section 4.3 demonstrate that observers' sensitivity to visual forms are altered by training, with corresponding changes to fMRI selectivity in higher dorsal and ventral visual regions being apparent. Analysis of the fMRI responses using pattern-based tuning functions showed in particular that training on low-signal visual stimuli increases the amplitude while decreasing the width of these pattern-based tuning functions in the higher dorsal and ventral visual regions.

The increase in amplitude of the pattern-based tuning functions following training indicates a higher discriminability of multivoxel representations of stimuli which may relate to enhanced neural responses to preferred stimulus categories at the level of neural populations across voxels. This finding is supported by further voxel-based tuning analysis which, while looking at the pooled behaviour averaged over the significant voxels within each visual region, finds similar increases in amplitude of the BOLD response to preferred stimuli after training.

The decrease in the width of the pattern-based tuning functions after training indicates fewer mis-predictions being made when classifying the stimuli. Further analysis of the mis-

predictions made show that this reduction manifests particularly at larger offsets from the preferred stimuli. This reduction in mis-predictions suggests learning decreases neural responses to non-preferred stimuli, which is further supported by the decrease in tuning width shown by the voxel-based tuning functions which directly model the BOLD response.

Our findings suggest that learning of visual patterns in the human visual cortex is

implemented by enhancing the response to preferred stimulus categories, while reducing the response to non-preferred stimulus categories.

The findings presented here, and covered in Appendix 1 by Zhang et al. (2010), advance understanding of the mechanisms which mediate learning in two main respects. First, while previous imaging studies (Kourtzi et al., 2005; Sigman et al., 2005; Op de Beeck et al., 2006; Mukai et al., 2007; Yotsumoto et al., 2008) have found evidence for changes in overall fMRI responsiveness to trained stimuli, the results discussed here provide evidence for learning

dependent changes related to neural selectivity. Second, the results presented here

demonstrate learning-dependent changes in fMRI selectivity in dorsal visual areas, which is consistent with the findings of previous work by members of the team which showed that these dorsal visual regions are involved in the integration of local orientation signals into global forms (Ostwald et al., 2008).

The fMRI analyses performed as a part of the study discussed in this chapter were facilitated by the use of the Matlab MVPA Toolbox described in Chapter 2 of this thesis. The toolbox provided analysis scripts for calculating the percentage signal change, performing 6-way pattern classification and performing the shuffling variant of the classification out of the box. The detailed results provided by these analyses, particularly the classification analysis,

provided all the details necessary for the development and application of the pattern-based tuning analysis, and the subsequent analysis of mis-predicted stimuli.

In addition to the pre-existing analyses implemented as part of the toolbox, the components provided by the toolbox for feature selection and time course pre-processing served to form the basis of the voxel-based tuning analysis which was implemented based on the description provided by Serences et al (2009).

This use of standardised analyses and analysis components, which have been employed in other published studies (Ban et al., 2012; Dövencioğlu et al., 2013) served to save in development time, allowing for a greater focus on development of novel analysis methods, while also providing the benefit of increased reliability due to repeated use and testing.

As a result the author of this thesis developed a novel method for the analysis of the tuning of the multi-variate encoding of visual stimuli based on the voxel tuning functions described by Serences et al. (2009), a method which allows for the investigation of the underlying

mechanisms involved in altering tuning while retaining the increased sensitivity provided by multi-voxel pattern analysis.

In summary, the study discussed in this chapter serves to increase understanding of the role of tuning in learning, provides a tool for the investigation of tuning at the level of multivariate patterns encoded by pools of voxels and gives an example of the utility of the MVPA toolbox presented in this thesis and its scope for use as a basis of further development.