Study shows monkeys are capable of complex deliberation and careful decision-making NatureNeuro
th chosen item in the target solution. We evaluated the accuracy using a held-out data set and confirmed that all RNNs achieved above 98% accuracy.To understand how the RNNs processed information during the deliberation period, we prematurely stopped the RNNs from deliberating and forced them to start selecting items. To do so, at a given time step during deliberation , we turned on the input that signifies the selection period.
$${\mathrm{random}}\,{\mathrm{score}}=\frac{1}{{|{{\Lambda }}|}}\mathop {\sum }\limits_{s \in {{\Lambda }}} \left$$$${{\Lambda }}=\left\{ {{{{\mathrm{S}}}}:\mathop {\sum }\limits_{x \in S} x \le {{{\mathrm{limit}}}}} \right\}.$$ $${\mathrm{number}}\,{\mathrm{of}}\,{\mathrm{good}}\,{\mathrm{solutions}}=\mathop {\sum }\limits_S I\left$$$$\tau _S=\left\{ {\begin{array}{*{20}{l}} {\mathop {\sum}\limits_{x \in S} x ,} \hfill & {if\,\mathop {\sum}\limits_{x \in S} x \le {\mathrm{limit}}} \hfill \\ {0,} \hfill & {if\,\mathop {\sum}\limits_{x \in S} x > {\mathrm{limit}}} \hfill \end{array}} \right.
$$\displaystyle \log \left}}{{1 - P\left}}} \right)=\beta _0 + \mathop {\sum}\limits_{i=1}^{10} {\beta _i \ast x_i + \left}$$
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