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The comparisons of accuracies between different networks are shown in Table 4. Briefly, we found that the second-to-last-layer activities of brain networks that were trained to do a given task had higher decoding accuracies than the baseline when we tried to decode information about a different task. That is, we found that a network trained to identify images actively retained information about space, and a network trained on a spatial task actively retained information about identity. In addition, the decoding accuracies were lower from the brain networks that were trained to do a different task than from brain networks that were trained to do the same task. Additional modeling to better understand why networks retained seemingly task-irrelevant information suggest that this information is retained and preserved uniquely in service of improving the accuracy of the “irrelevant” task. For example, the identity network actively maintained more information about orientation than location because in order to determine whether the object is in the unscrambled or scrambled order, the network needs to determine the object orientation. Finally, simulation results from comparing a single combined pathway versus two segregated pathways in order to accurately identify objects and accurately determine the location and orientation of objects suggest that two separate pathways are advantageous in order to process the same input (visual information) in different ways for different tasks or goals. The specific comparisons and findings are discussed in more detail in section 4.

Table 4:

Comparisons of Testing Accuracies between Different Networks.

Network 1Network 2Average Difference in Accuracy (%) (*p<0.05, **p<0.01, ***p<0.001)p-Value
network(identity,space) networkspacebaseline 27.8*** <0.001 
network(identity,space) network(space,space) −10.7*** <0.001 
network(identity,space) network(shoes,space) 14.8*** <0.001 
network(location,identity) network(space,identity) −6.2*** <0.001 
network(orientation,identity) network(space,identity) 1.0 0.324 
network(location,identity) network(orientation,identity) −7.2*** <0.001 
network(space,identity) networkidentitybaseline 10.5*** <0.001 
network(space,identity) network(identity,identity) −10.4*** <0.001 
network(space,shoes) networkshoesbaseline −3.2** 0.005 
networksseparateidentityandspace networkcombineidentityandspace 4.0*** <0.001 
Network 1Network 2Average Difference in Accuracy (%) (*p<0.05, **p<0.01, ***p<0.001)p-Value
network(identity,space) networkspacebaseline 27.8*** <0.001 
network(identity,space) network(space,space) −10.7*** <0.001 
network(identity,space) network(shoes,space) 14.8*** <0.001 
network(location,identity) network(space,identity) −6.2*** <0.001 
network(orientation,identity) network(space,identity) 1.0 0.324 
network(location,identity) network(orientation,identity) −7.2*** <0.001 
network(space,identity) networkidentitybaseline 10.5*** <0.001 
network(space,identity) network(identity,identity) −10.4*** <0.001 
network(space,shoes) networkshoesbaseline −3.2** 0.005 
networksseparateidentityandspace networkcombineidentityandspace 4.0*** <0.001 

Notes: The first two sections examine whether and why there is information about space in the identity network. The next section examines whether there is information about identity and what kind of identity information is in the space network. The final section compares testing accuracies of a network doing the identity and spatial tasks using two separate pathways with a network doing the identity and spatial tasks using a single pathway.

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