Neural Models of Temporally Organized Behaviors: Handwriting Production and Working Memory

Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309

On becoming a physicist of mind

In 1976, the German Max Planck Society established a new research enterprise in psycholinguistics, which became the Max Planck Institute for Psycholinguistics in Nijmegen, the Netherlands. I was fortunate enough to be invited to direct this institute. It enabled me, with my background in visual and auditory psychophysics and the theory of formal grammars and automata, to develop a long-term chronometric endeavor to dissect the process of speaking. It led, among other work, to my book Speaking (1989) and to my research team's article in Brain and Behavioral Sciences “A Theory of Lexical Access in Speech Production” (1999). When I later became president of the Royal Netherlands Academy of Arts and Sciences, I helped initiate the Women for Science research project of the Inter Academy Council, a project chaired by my physicist sister at the National Institute of Standards and Technology. As an emeritus I published a comprehensive History of Psycholinguistics (2013). As will become clear, many people inspired and joined me in these undertakings

Environmental modeling and recognition for an autonomous land vehicle

An architecture for object modeling and recognition for an autonomous land vehicle is presented. Examples of objects of interest include terrain features, fields, roads, horizon features, trees, etc. The architecture is organized around a set of data bases for generic object models and perceptual structures, temporary memory for the instantiation of object and relational hypotheses, and a long term memory for storing stable hypotheses that are affixed to the terrain representation. Multiple inference processes operate over these databases. Researchers describe these particular components: the perceptual structure database, the grouping processes that operate over this, schemas, and the long term terrain database. A processing example that matches predictions from the long term terrain model to imagery, extracts significant perceptual structures for consideration as potential landmarks, and extracts a relational structure to update the long term terrain database is given

SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequences to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.Riverside Reserach Institute; Defense Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI 90-24877, SBE-0345378); Office of Naval Research (N00014-92-J-1309, N00014-91-J-4100, N00014-01-1-0624, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2

Representing space for practical reasoning

This paper describes a new approach to representing space and time for practical reasoning, based on space-filling cells. Unlike R n, the new models can represent a bounded region of space using only finitely many cells, so they can be manipulated directly. Unlike Z n, they have useful notions of function continuity and region connectedness. The topology of space is allowed to depend on the situation being represented, accounting for sharp changes in function values and lack of connectedness across object boundaries. Algorithms based on this model of space are neither purely region-based nor purely boundary-based, but a blend of the two. This new style of algorithm design is illustrated by a new program for finding edges in grey-scale images. Although the program is based on a relatively conventional second directional difference operator, it can detect fine texture in the presence of camera noise, produce connected boundaries around sharp corners, and return thin boundaries without "feathering. " New algorithms are presented for combining directional differences, suppressing the effects of camera noise, reconstructing image intensities from the second difference values and merging results from different scales (including suppression of spurious boundaries in staircase patterns).

Two brains in action: joint-action coding in the primate frontal cortex

Daily life often requires the coordination of our actions with those of another partner. After sixty years (1968-2018) of behavioral neurophysiology of motor control, the neural mechanisms which allow such coordination in primates are unknown. We studied this issue by recording cell activity simultaneously from dorsal premotor cortex (PMd) of two male interacting monkeys trained to coordinate their hand forces to achieve a common goal. We found a population of 'joint-action cells' that discharged preferentially when monkeys cooperated in the task. This modulation was predictive in nature, since in most cells neural activity led in time the changes of the "own" and of the "other" behavior. These neurons encoded the joint-performance more accurately than 'canonical action-related cells', activated by the action per se, regardless of the individual vs. interactive context. A decoding of joint-action was obtained by combining the two brains activities, using cells with directional properties distinguished from those associated to the 'solo' behaviors. Action observation-related activity studied when one monkey observed the consequences of the partner's behavior, i.e. the cursor's motion on the screen, did not sharpen the accuracy of 'joint-action cells' representation, suggesting that it plays no major role in encoding joint-action. When monkeys performed with a non-interactive partner, such as a computer, 'joint-action cells' representation of the "other" (non-cooperative) behavior was significantly degraded. These findings provide evidence of how premotor neurons integrate the time-varying representation of the self-action with that of a co-actor, thus offering a neural substrate for successful visuo-motor coordination between individuals.SIGNIFICANT STATEMENTThe neural bases of inter-subject motor coordination were studied by recording cell activity simultaneously from the frontal cortex of two interacting monkeys, trained to coordinate their hand forces to achieve a common goal. We found a new class of cells, preferentially active when the monkeys cooperated, rather than when the same action was performed individually. These 'joint-action neurons' offered a neural representation of joint-behaviors by far more accurate than that provided by the canonical action-related cells, modulated by the action per se regardless of the individual/interactive context. A neural representation of joint-performance was obtained by combining the activity recorded from the two brains. Our findings offer the first evidence concerning neural mechanisms subtending interactive visuo-motor coordination between co-acting agents