Perceptual and conceptual processing of visual objects across the adult lifespan

Abstract

Making sense of the external world is vital for multiple domains of cognition, and so it is crucial that object recognition is maintained across the lifespan. We investigated age differences in perceptual and conceptual processing of visual objects in a population-derived sample of 85 healthy adults (24-87 years old) by relating measures of object processing to cognition across the lifespan. Magnetoencephalography (MEG) was recorded during a picture naming task to provide a direct measure of neural activity, that is not confounded by age-related vascular changes. Multiple linear regression was used to estimate neural responsivity for each individual, namely the capacity to represent visual or semantic information relating to the pictures. We find that the capacity to represent semantic information is linked to higher naming accuracy, a measure of task-specific performance. In mature adults, the capacity to represent semantic information also correlated with higher levels of fluid intelligence, reflecting domain-general performance. In contrast, the latency of visual processing did not relate to measures of cognition. These results indicate that neural responsivity measures relate to naming accuracy and fluid intelligence. We propose that maintaining neural responsivity in older age confers benefits in task-related and domain-general cognitive processes, supporting the brain maintenance view of healthy cognitive ageing.

Figure 1

Behavioural results for (a) naming accuracy, (b) reaction times, (c) Spot the Word and (d) Cattell Culture Fair versus age.

Figure 2

Schematic representation of the analysis pipeline. Calculation of (a) visual model fit, (b) semantic model fit and (c) peak latency. See method section for details.

Figure 3

Model fits across time showing R² values for the (abcd) visual and (efgh) semantic model for the (ae) young, (bf) middle-aged and (cg) mature groups for all sensors and averaged across sensors for the three age groups (dh). Note that the effect sizes cannot be directly compared, as the visual model fit is calculated on the raw MEG signal and the semantic model fit is calculated on the residuals after the visual model fits are regressed out (see methods and Fig. 2).

Figure 4

Topographies of visual model fit at 110 ms after stimulus onset, the mean peak latency, (abc) and semantic model fit at 290 ms after stimulus onset, derived from Clarke et al. (2015) as time with maximal classification accuracy for the semantic model, (def). Topographies for magnetometers gradiometers are visualized in the young (ad), middle-aged (be) and mature (cf) age groups.

Figure 5

Relationship between the visual model fit, the semantic model fit, age and accuracy. (a) Correlation between age and the visual model fit, (b) Correlation between age and the semantic model fit, (c) Correlation between visual and semantic model fit (corrected for age), (d) Correlation between accuracy and semantic model fit (corrected for age).

Figure 6

Prediction of fluid intelligence: (ab) interaction between age and the semantic model fit. (a) The interaction effect is visualized by generation of the predicted Cattell Score for every combination of age and semantic model fit based on the interaction term from the moderation model. (b) The correlation within the young, middle-aged and mature group.

Figure 7

Relationship between the peak latency, age and the visual and semantic model fits. (a) Correlation between age and peak latency, (b) Correlation between peak latency and visual model fit (corrected for age), (c) Correlation between peak latency and semantic model fit (corrected for age).