Fig 1.
(A) The relationship between the neural coding scheme of orientations (colours) in WM over time, illustrated in neural state space (reduced to three dimensions, for visualisation). Left: A stable coding scheme within a stable neural population (defined by dimensions 1 and 2; dimension 3 has no meaningful variance). Middle: A stable coding scheme (dimensions 1 and 2) within a dynamic neural population (dimension 3). Right: A dynamically changing coding scheme (coding for orientation and time is mixed across dimensions). (B) The fidelity of the population code in WM over time. Top: The code decays and becomes less specific over time, leading to random errors during readout. Bottom: The code drifts along the feature dimension, leading to a still sharp, but shifted code during readout. WM, working memory.
Fig 2.
Trial schematic and behavioural results.
(A) Two randomly orientated grating stimuli were presented laterally. A retro-cue then indicated which of those two would be tested at the end of the trial. Two impulses (white circles) were serially presented in the subsequent delay period. At the end of the trial, a randomly oriented probe grating was presented in the centre of the screen, and participants were instructed to rotate this probe until it reflected the cued orientation. (B) Report errors of all trials across all participants. Data available at osf.io/cn8zf.
Fig 3.
Top row: Normalised average pattern similarity (mean-centred, sign-reversed Mahalanobis distance) of the evoked neural responses (100 to 400 ms relative to stimulus onset) as a function of orientation similarity, and decoding accuracy (cosine vector means of pattern similarities). Error shadings and error bars are 95% CI of the mean. Asterisks indicate significant decoding accuracies (p < 0.05, one-sided) or differences (p < 0.05, two-sided). Bottom row: Decoding topographies of the searchlight analysis. Posterior channels used in all other decoding analyses are highlighted. Ipsilateral (‘ipsi.’) and contralateral (‘contr.’) channels used to test for item lateralisation are highlighted in turquoise and pink, respectively. Data available at osf.io/cn8zf. norm. volt., normalised voltage; n.s., not significant.
Fig 4.
Cross-generalisation of coding scheme between impulses.
(A) Visualisation of orientation and impulse code in state space. The first dimension discriminates between impulses. The second and third dimensions code the orientation space in both impulses. (B) Trial-wise accuracy (%) of impulse decoding. (C) Orientation decoding within each impulse (blue) and orientation code cross-generalisation between impulses (green). Error shadings and error bars are 95% CI of the mean. Asterisks indicate significant decoding accuracies or cross-generalisation (p < 0.05). Data available at osf.io/cn8zf. a.u., arbitrary units; norm. volt., normalised voltage; n.s., not significant.
Fig 5.
No cross-generalisation of coding scheme between cued item locations during impulse responses.
(A) Visualisation of orientation and item location code in state space. The first dimension discriminates between item locations. The first and second dimensions code the orientation space, separately for WM items previously presented on the left or right side. (B) Trial-wise accuracy (%) of item location decoding. (C) Orientation decoding within each item location (blue) and orientation code cross-generalising between different item locations (green). Error shadings and error bars are 95% CI of the mean. Asterisks indicate significant decoding accuracies and differences (p < 0.05). Data available at osf.io/cn8zf. a.u., arbitrary units; norm. volt., normalised voltage; n.s., not significant; WM, working memory.
Fig 6.
Response-dependent averaging of trial-wise similarity profiles demonstrates drift.
Schematic and results. (A) Testing for shift towards response by averaging trial-wise similarity profiles by CCW/CW responses. (B) Results of schematised approach in (A). Orientation similarity profiles averaged by response such that a rightward shift reflects a shift towards the response (purple) at each event. Purple vertical lines show circular means of the similarity profiles. Insets show orientation similarity profiles for CCW (blue) and CW (green) responses separately. Error shadings are 95% CI of the mean. (C) Group-level (circular mean) and participant-level (asymmetry score) shifts towards the response of each response-dependent similarity profile are shown in black and grey, respectively. Error bars are 95% CI of the mean. The blue line and shading indicates the mean and 95% CI of the absolute, bias-adjusted behavioural response deviation (approximately 10 degrees). Data available at osf.io/cn8zf. a.u., arbitrary units; CCW, counterclockwise; CW, clockwise; norm. volt., normalised; n.s., not significant.
Fig 7.
Response-dependent training and testing demonstrates drift.
Schematic and results. (A) Testing for shift towards response by first splitting the neuroimaging data into CW and CCW data sets and training on CW trials and testing on CCW trials, and vice versa. Given an actual shift, the shift of the resulting orientation reconstruction will be doubled, since training and testing data are shifted in opposite directions. (B) Results of schematised approach in (A). Average orientation similarity profiles such that a rightward shift reflects a shift towards the response (purple) at each event. Purple vertical lines show circular means of the similarity profiles. Insets show orientation similarity profiles for CCW (blue) and CW (green) responses separately. Error shadings are 95% CI of the mean. (C) Group-level (circular mean) and participant-level (asymmetry score) shifts towards the response of each response-dependent similarity profile are shown in black and grey, respectively. Error bars are 95% CI of the mean. Data available at osf.io/cn8zf. a.u., arbitrary units; CCW, counterclockwise; CW, clockwise; norm. volt., normalised voltage; n.s., not significant.