Learning Shapes the Representation of Visual Categories in the Aging Human Brain

Abstract

Abstract■ The ability to make categorical decisions and interpret sensory experiences is critical for survival and interactions across the lifespan. However, little is known about the human brain mechanisms that mediate the learning and representation of visual categories in aging. Here we combine behavioral measurements and fMRI measurements to investigate the neural processes that mediate flexible category learning in the aging human brain. Our findings show that training changes the decision criterion (i.e., categorical boundary) that young and older observers use for making categorical judgments. Comparing the behavioral choices of human observers with those of a pattern classifier on the basis of multivoxel fMRI signals, we demonstrate learning-dependent changes in similar cortical areas for young and older adults. In particular, we show that neural signals in occipito-temporal and posterior parietal regions change through learning to reflect the perceived visual categories. Information in these areas about the perceived visual categories is preserved in aging, whereas information content is compromised in more anterior parietal and frontal circuits. Thus, these findings provide novel evidence for flexible category learning in aging that shapes the neural representations of visual categories to reflect the observersʼ behavioral judgments. ■

Coding of Visual, Auditory, Rule, and Response Information in the Brain: 10 Years of Multivoxel Pattern Analysis

“…In one study, radial and concentric patterns had been associated with differential button presses during training, although during scanning, participants performed an unrelated task (Mayhew et al, 2010). In all other cases, any button press responses given by participants were orthogonal (Mayhew & Kourtzi, 2013) or unrelated (Pollmann et al, 2014;Reverberi et al, 2012a;Kalberlah et al, 2011;Mayhew et al, 2010) to the visual discrimination.…”

“…We excluded any papers in which there were obvious associations between our task features, and in our stricter analysis, we also excluded any studies in which higher-level features such as semantic category differed between decoded items, or cases where items might evoke representations of associated motor actions. The remaining points of visual discrimination in the motor cortex were for discrimination between Gabor patches differing in color and spatial frequency (Pollmann, Zinke, Baumgartner, Geringswald, & Hanke, 2014), the spatial location of a target (Kalberlah, Chen, Heinzle, & Haynes, 2011), radial versus concentric glass patterns (Mayhew & Kourtzi, 2013;Mayhew, Li, Storrar, Tsvetanov, & Kourtzi, 2010), and between two abstract shapes cuing the same rule (Reverberi, Gorgen, & Haynes, 2012a). In one study, radial and concentric patterns had been associated with differential button presses during training, although during scanning, participants performed an unrelated task (Mayhew et al, 2010).…”

“…The remaining points of visual discrimination in the motor cortex were for discrimination between Gabor patches differing in color and spatial frequency (Pollmann, Zinke, Baumgartner, Geringswald, & Hanke, 2014), the spatial location of a target (Kalberlah, Chen, Heinzle, & Haynes, 2011), radial versus concentric glass patterns (Mayhew & Kourtzi, 2013;Mayhew, Li, Storrar, Tsvetanov, & Kourtzi, 2010), and between two abstract shapes cuing the same rule (Reverberi, Gorgen, & Haynes, 2012a). In one study, radial and concentric patterns had been associated with differential button presses during training, although during scanning, participants performed an unrelated task (Mayhew et al, 2010). In all other cases, any button press responses given by participants were orthogonal (Mayhew & Kourtzi, 2013) or unrelated (Pollmann et al, 2014;Reverberi et al, 2012a;Kalberlah et al, 2011;Mayhew et al, 2010) to the visual discrimination.…”

Learning Acts on Distinct Processes for Visual Form Perception in the Human Brain

“…For all stimulus patterns, the dot density was 3% and the size of each dot was 2.3 ϫ 2.3 arc min. These parameters were chosen based on pilot psychophysical studies and in accordance with previous studies (Li et al, 2009;Mayhew et al, 2010a) showing that coherent form patterns are reliably perceived for these parameters. We generated radial (0°spiral angle) and concentric (90°spiral angle) Glass patterns by placing dipoles tangentially (concentric stimuli) or orthogonally (radial stimuli) to the circumference of a circle centered on the fixation dot.…”

Recent developments in multivariate pattern analysis for functional MRI

“…Applications of the MVPA have rapidly developed, covering studies of neuronal mechanisms in various domains, such as perception [12][13][14][15][16][17] , learning and memory [18][19][20][21] , language [22] , intention [23] , decision-making [5,[24][25][26] , emotion [27][28][29][30] , and mental disorders [31][32][33] . Instead of serving as a tutorial on applying multivariate classifiers to fMRI data (see Pereira et al [34] , an excellent reference for this purpose), this review attempts to give a general introduction to recent promising developments branching off from the original idea of treating fMRI data as multivariate patterns.…”