explicit memory theory Flashcards
hippocampus important for what tasks (2)
- spatial tasks
- relational memory
what is relational memory
memory of relationships bw objects and events
fundamental functions of hippocampal network (2)
- spatial and memory problems can both benefit from structural abstractions (give a structure to sensory info)
- figure out how best to organize sensory events into abstract structure (graph or map) for the purpose of sensory predictions
what does neural activity in entorhinal cortex correlate with (animal behavior) (2)
- speed of animal as it walks around (speed cells)
- direction the animal is facing (head direction cells)
input to (a) speed cells (b) head direction cells
(a) proprioception, visual
(b) angular velocity of head shifts pattern of activity (change in direction)
how does spatial info create a map
path integration (by creating vector that represents position and direction in comparison to earlier reference point)
what are abstract structures, such as maps, used for
organizing sequences of sensory information for the purpose of prediction
how is map formation similar to semantic memory formation and what does it suggests
both are a two-step process (remembering life episodes/spatial info + inferring info from what you have already?)
* suggests that maybe neuronal algorithms of map formation are the same as the ones underlying explicit memory formation (neuronal algorithms of real and mental space are the same)
what are cognitive maps and what are they required for (3)
systematic organization of knowledge required for (a) speed of learning (b) ability to generalize (c) ability to make complex inferences
what do cognitive maps directly aid in
construction and accumulation of spatial knowledge, allowing the ‘mind’s eye’ to visualize scene or event
how can spatial thinking also be used for non-spatial tasks
non-spatial info can often be organized in abstract structures (word clouds)
deep neural net machine learning algorithms that are used for pattern recognition have (3)
- structure -> # of layers and nodes, how each node gets activated (inputs, outputs)
- objective function -> goal (like label things in input or predict what next input will be or identify best action); has clear right and wrong answer
- learning function -> method of adjusting strength of each connection to better achieve objective function (backpropagation technique) (what gets me closer to the right answer?)
how recommender systems worked before and how they work now
before -> asked questions to user (do you like comedy? romance?); give a score on how much would like movie
now -> have a matrix of scattered info of likes; program creates matrixes (of likes of people and movie recs) that if multiplied, gives input matrix; fills in matrix (infers) and adjusts connections
what do all spatial environments share
common regularities of euclidean space that define which inferences can be made, and which shortcuts might exist (like moving S->E->N->W brings you back to where you started)
ex of how structural regularities can apply to non-spatial relational problems (2)
- transitive inference problems (depend on hippocampus) require stimuli to be represented on abstract ordered line (A > B and B > C implies that A > C)
- abstraction of hierarchical structure permits rapid inferences when encountering new social situations (learn structure, fill in labels)
what structural relationships can be formalized on a connected graph (3)
- social hierarchies
- transitive inferences
- spatial reasoning
main input pathway to hippocampus
entorhinal cortex in temporal lobe
what does (a) MEC; (b) LEC provide to hippocampus
- learned structural info
- perceived sensory info (what experienced at the time)
role of hippocampus in relation to MEC and LEC
hippocampus combines structural info from MEC and sensory info from LEC (associates sensory info with place -> position in coordinate map or place in abstract theoretical structure)
what does our brain do to account for speed of learning, ability to generalize and ability to make complex inferences
identifies how sensory info is structured, whether on spatial map or in abstract space
what does factorized representation of sensory experiences enable (2)
- flexible recombination of info to represent novel experiences
- generalizing structural knowledge to new situations
structure of task to create a machine learning algorithm to test factorization idea (3)
- function of machine is to predict what it will see after taking a step in the indicated direction
- info provided at each step is (a) directional cue and (b) sensory stimulus
- job of machine is to predict sensory stimulus from directional cue
consequence of (a) not knowing structure of space and of (b) knowing structure of space
(a) have to memorize each transition -> have to enter each spot from each direction; if object appears in different places, multi-step memories are needed, exponentially increases memory load
(b) only need to visit each spot once, from any direction -> may be initially hard to learn underlying structure, but once learned, can be applied to new situations
aspects of supervised learning (4) + ex
- identify how a pattern of inputs related to a pattern of outputs using real-life examples
- training data consists of input-output pairings -> figure out how to adjust synaptic weights to get right answer
- training data is labeled
- used for recognition tasks
* ex = train machine to read hand-written cheques; give it a million cheques and tell it exactly what is written on them (real person has to give answers, time consuming); if good input-output function learned, able to read new cheques wasn’t trained on)
