One Size Does Not Fit All: Idiographic Computational Models Reveal Individual Differences in Learning and Meta-Learning Strategies.

Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long-term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT-R models of performance on the stimulus-response learning, Reinforcement Learning Working Memory task. The task consisted of short 3-image, and long 6-image, feedback-based learning blocks. A no-feedback test phase was administered after learning, with an interfering task inserted between learning and test. Our four models included two single-mechanism RL and LTM models, and two integrated RL-LTM models: (a) RL-based meta-learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL-LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta-RL: 13.25%, bias-RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus-response task. Finally, long-term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3- vs 6-image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated-models, owing to the strength of our idiographic approach.

Visual stimulus-driven functional organization of macaque prefrontal cortex.

The extent to which the major subdivisions of prefrontal cortex (PFC) can be functionally partitioned is unclear. In approaching the question, it is often assumed that the organization is task dependent. Here we use fMRI to show that PFC can respond in a task-independent way, and we leverage these responses to uncover a stimulus-driven functional organization. The results were generated by mapping the relative location of responses to faces, bodies, scenes, disparity, color, and eccentricity in four passively fixating macaques. The results control for individual differences in functional architecture and provide the first account of a systematic visual stimulus-driven functional organization across PFC. Responses were focused in dorsolateral PFC (DLPFC), in the ventral prearcuate region; and in ventrolateral PFC (VLPFC), extending into orbital PFC. Face patches were in the VLPFC focus and were characterized by a striking lack of response to non-face stimuli rather than an especially strong response to faces. Color-biased regions were near but distinct from face patches. One scene-biased region was consistently localized with different contrasts and overlapped the disparity-biased region to define the DLPFC focus. All visually responsive regions showed a peripheral visual-field bias. These results uncover an organizational scheme that presumably constrains the flow of information about different visual modalities into PFC.

Color statistics of objects, and color tuning of object cortex in macaque monkey.

We hypothesized that the parts of scenes identified by human observers as "objects" show distinct color properties from backgrounds, and that the brain uses this information towards object recognition. To test this hypothesis, we examined the color statistics of naturally and artificially colored objects and backgrounds in a database of over 20,000 images annotated with object labels. Objects tended to be warmer colored (L-cone response > M-cone response) and more saturated compared to backgrounds. That the distinguishing chromatic property of objects was defined mostly by the L-M post-receptoral mechanism, rather than the S mechanism, is consistent with the idea that trichromatic color vision evolved in response to a selective pressure to identify objects. We also show that classifiers trained using only color information could distinguish animate versus inanimate objects, and at a performance level that was comparable to classification using shape features. Animate/inanimate is considered a fundamental superordinate category distinction, previously thought to be computed by the brain using only shape information. Our results show that color could contribute to animate/inanimate, and likely other, object-category assignments. Finally, color-tuning measured in two macaque monkeys with functional magnetic resonance imaging (fMRI), and confirmed by fMRI-guided microelectrode recording, supports the idea that responsiveness to color reflects the global functional organization of inferior temporal cortex, the brain region implicated in object vision. More strongly in IT than in V1, colors associated with objects elicited higher responses than colors less often associated with objects.